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Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20240304 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20240304 Schema Browser (other Browser versions).
A B C D E F G H I J K L M
N O P Q R S T U V W X Y Z

J

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified Content Descriptor
J twomass SIXDF J magnitude (JEXT) used for J selection real 4 mag    
j_1AperMag1 vvvSource VVVDR5 Point source J_1 aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMag1Err vvvSource VVVDR5 Error in point source J_1 mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1AperMag3 vikingSource VIKINGv20151230 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vikingSource VIKINGv20160406 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vikingSource VIKINGv20161202 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vikingSource VIKINGv20170715 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMag3 vvvSource VVVDR5 Default point source J_1 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMag3Err vikingSource VIKINGv20151230 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vikingSource VIKINGv20160406 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vikingSource VIKINGv20161202 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vikingSource VIKINGv20170715 Error in default point/extended source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag3Err vvvSource VVVDR5 Error in default point source J_1 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1AperMag4 vikingSource VIKINGv20151230 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vikingSource VIKINGv20160406 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vikingSource VIKINGv20161202 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vikingSource VIKINGv20170715 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag4 vvvSource VVVDR5 Point source J_1 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_1AperMag4Err vikingSource VIKINGv20151230 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vikingSource VIKINGv20160406 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vikingSource VIKINGv20161202 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vikingSource VIKINGv20170715 Error in point/extended source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag4Err vvvSource VVVDR5 Error in point source J_1 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_1AperMag6 vikingSource VIKINGv20151230 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6 vikingSource VIKINGv20160406 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6 vikingSource VIKINGv20161202 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6 vikingSource VIKINGv20170715 Point source J_1 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMag6Err vikingSource VIKINGv20151230 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag6Err vikingSource VIKINGv20160406 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag6Err vikingSource VIKINGv20161202 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMag6Err vikingSource VIKINGv20170715 Error in point/extended source J_1 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20151230 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20160406 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20161202 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr3 vikingSource VIKINGv20170715 Default extended source J_1 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20151230 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20160406 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20161202 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr4 vikingSource VIKINGv20170715 Extended source J_1 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20151230 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20160406 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20161202 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AperMagNoAperCorr6 vikingSource VIKINGv20170715 Extended source J_1 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_1AverageConf vikingSource VIKINGv20151230 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4   -0.9999995e9 stat.likelihood
j_1AverageConf vikingSource VIKINGv20160406 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4   -0.9999995e9 stat.likelihood
j_1AverageConf vikingSource VIKINGv20161202 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4   -0.9999995e9 stat.likelihood
j_1AverageConf vikingSource VIKINGv20170715 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4   -0.9999995e9 stat.likelihood
j_1AverageConf vvvSource VVVDR5 average confidence in 2 arcsec diameter default aperture (aper3) J_1 real 4   -0.9999995e9 stat.likelihood;em.IR.J
j_1Class vikingSource VIKINGv20151230 discrete image classification flag in J_1 smallint 2   -9999 src.class
j_1Class vikingSource VIKINGv20160406 discrete image classification flag in J_1 smallint 2   -9999 src.class
j_1Class vikingSource VIKINGv20161202 discrete image classification flag in J_1 smallint 2   -9999 src.class
j_1Class vikingSource VIKINGv20170715 discrete image classification flag in J_1 smallint 2   -9999 src.class
j_1Class vvvSource VVVDR5 discrete image classification flag in J_1 smallint 2   -9999 src.class;em.IR.J
j_1ClassStat vikingSource VIKINGv20151230 N(0,1) stellarness-of-profile statistic in J_1 real 4   -0.9999995e9 stat
j_1ClassStat vikingSource VIKINGv20160406 N(0,1) stellarness-of-profile statistic in J_1 real 4   -0.9999995e9 stat
j_1ClassStat vikingSource VIKINGv20161202 N(0,1) stellarness-of-profile statistic in J_1 real 4   -0.9999995e9 stat
j_1ClassStat vikingSource VIKINGv20170715 N(0,1) stellarness-of-profile statistic in J_1 real 4   -0.9999995e9 stat
j_1ClassStat vvvSource VVVDR5 S-Extractor classification statistic in J_1 real 4   -0.9999995e9 stat;em.IR.J
j_1Ell vikingSource VIKINGv20151230 1-b/a, where a/b=semi-major/minor axes in J_1 real 4   -0.9999995e9 src.ellipticity
j_1Ell vikingSource VIKINGv20160406 1-b/a, where a/b=semi-major/minor axes in J_1 real 4   -0.9999995e9 src.ellipticity
j_1Ell vikingSource VIKINGv20161202 1-b/a, where a/b=semi-major/minor axes in J_1 real 4   -0.9999995e9 src.ellipticity
j_1Ell vikingSource VIKINGv20170715 1-b/a, where a/b=semi-major/minor axes in J_1 real 4   -0.9999995e9 src.ellipticity
j_1Ell vvvSource VVVDR5 1-b/a, where a/b=semi-major/minor axes in J_1 real 4   -0.9999995e9 src.ellipticity;em.IR.J
j_1eNum vikingMergeLog VIKINGv20151230 the extension number of this J_1 frame tinyint 1     meta.number
j_1eNum vikingMergeLog VIKINGv20160406 the extension number of this J_1 frame tinyint 1     meta.number
j_1eNum vikingMergeLog VIKINGv20161202 the extension number of this J_1 frame tinyint 1     meta.number
j_1eNum vikingMergeLog VIKINGv20170715 the extension number of this J_1 frame tinyint 1     meta.number
j_1eNum vvvMergeLog VVVDR5 the extension number of this J_1 frame tinyint 1     meta.number;em.IR.J
j_1eNum vvvPsfDophotZYJHKsMergeLog VVVDR5 the extension number of this 1st epoch J frame tinyint 1     meta.number;em.IR.J
j_1ErrBits vikingSource VIKINGv20151230 processing warning/error bitwise flags in J_1 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vikingSource VIKINGv20160406 processing warning/error bitwise flags in J_1 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vikingSource VIKINGv20161202 processing warning/error bitwise flags in J_1 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vikingSource VIKINGv20170715 processing warning/error bitwise flags in J_1 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1ErrBits vvvSource VVVDR5 processing warning/error bitwise flags in J_1 int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_1Eta vikingSource VIKINGv20151230 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vikingSource VIKINGv20160406 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vikingSource VIKINGv20161202 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vikingSource VIKINGv20170715 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Eta vvvSource VVVDR5 Offset of J_1 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Gausig vikingSource VIKINGv20151230 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vikingSource VIKINGv20160406 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vikingSource VIKINGv20161202 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vikingSource VIKINGv20170715 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param
j_1Gausig vvvSource VVVDR5 RMS of axes of ellipse fit in J_1 real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
j_1HlCorSMjRadAs vikingSource VIKINGv20151230 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1HlCorSMjRadAs vikingSource VIKINGv20160406 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1HlCorSMjRadAs vikingSource VIKINGv20161202 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1HlCorSMjRadAs vikingSource VIKINGv20170715 Seeing corrected half-light, semi-major axis in J_1 band real 4 arcsec -0.9999995e9 phys.angSize
j_1mfID vikingMergeLog VIKINGv20151230 the UID of the relevant J_1 multiframe bigint 8     meta.id;obs.field
j_1mfID vikingMergeLog VIKINGv20160406 the UID of the relevant J_1 multiframe bigint 8     meta.id;obs.field
j_1mfID vikingMergeLog VIKINGv20161202 the UID of the relevant J_1 multiframe bigint 8     meta.id;obs.field
j_1mfID vikingMergeLog VIKINGv20170715 the UID of the relevant J_1 multiframe bigint 8     meta.id;obs.field
j_1mfID vvvMergeLog VVVDR5 the UID of the relevant J_1 multiframe bigint 8     meta.id;obs.field;em.IR.J
j_1mfID vvvPsfDophotZYJHKsMergeLog VVVDR5 the UID of the relevant 1st epoch J tile multiframe bigint 8     meta.id;obs.field;em.IR.J
j_1mh_1Pnt vvvSource VVVDR5 Point source colour J_1-H_1 (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mh_1PntErr vvvSource VVVDR5 Error on point source colour J_1-H_1 real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20151230 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20160406 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20161202 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExt vikingSource VIKINGv20170715 Extended source colour J_1-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20151230 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20160406 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20161202 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhExtErr vikingSource VIKINGv20170715 Error on extended source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20151230 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20160406 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20161202 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPnt vikingSource VIKINGv20170715 Point source colour J_1-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20151230 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20160406 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20161202 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1mhPntErr vikingSource VIKINGv20170715 Error on point source colour J_1-H real 4 mag -0.9999995e9 stat.error;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_1Mjd vikingSource VIKINGv20151230 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vikingSource VIKINGv20160406 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vikingSource VIKINGv20161202 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vikingSource VIKINGv20170715 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch
j_1Mjd vvvPsfDophotZYJHKsMergeLog VVVDR5 the MJD of the 1st epoch J tile multiframe float 8     time;em.IR.J
j_1Mjd vvvSource VVVDR5 Modified Julian Day in J_1 band float 8 days -0.9999995e9 time.epoch;em.IR.J
j_1PA vikingSource VIKINGv20151230 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vikingSource VIKINGv20160406 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vikingSource VIKINGv20161202 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vikingSource VIKINGv20170715 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng
j_1PA vvvSource VVVDR5 ellipse fit celestial orientation in J_1 real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
j_1PetroMag vikingSource VIKINGv20151230 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMag vikingSource VIKINGv20160406 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMag vikingSource VIKINGv20161202 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMag vikingSource VIKINGv20170715 Extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_1PetroMagErr vikingSource VIKINGv20151230 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PetroMagErr vikingSource VIKINGv20160406 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PetroMagErr vikingSource VIKINGv20161202 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PetroMagErr vikingSource VIKINGv20170715 Error in extended source J_1 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1ppErrBits vikingSource VIKINGv20151230 additional WFAU post-processing error bits in J_1 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vikingSource VIKINGv20160406 additional WFAU post-processing error bits in J_1 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vikingSource VIKINGv20161202 additional WFAU post-processing error bits in J_1 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vikingSource VIKINGv20170715 additional WFAU post-processing error bits in J_1 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_1ppErrBits vvvSource VVVDR5 additional WFAU post-processing error bits in J_1 int 4   0 meta.code;em.IR.J
j_1PsfMag vikingSource VIKINGv20151230 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMag vikingSource VIKINGv20160406 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMag vikingSource VIKINGv20161202 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMag vikingSource VIKINGv20170715 Point source profile-fitted J_1 mag real 4 mag -0.9999995e9 phot.mag
j_1PsfMagErr vikingSource VIKINGv20151230 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PsfMagErr vikingSource VIKINGv20160406 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PsfMagErr vikingSource VIKINGv20161202 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1PsfMagErr vikingSource VIKINGv20170715 Error in point source profile-fitted J_1 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SeqNum vikingSource VIKINGv20151230 the running number of the J_1 detection int 4   -99999999 meta.number
j_1SeqNum vikingSource VIKINGv20160406 the running number of the J_1 detection int 4   -99999999 meta.number
j_1SeqNum vikingSource VIKINGv20161202 the running number of the J_1 detection int 4   -99999999 meta.number
j_1SeqNum vikingSource VIKINGv20170715 the running number of the J_1 detection int 4   -99999999 meta.number
j_1SeqNum vvvSource VVVDR5 the running number of the J_1 detection int 4   -99999999 meta.number;em.IR.J
j_1SerMag2D vikingSource VIKINGv20151230 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2D vikingSource VIKINGv20160406 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2D vikingSource VIKINGv20161202 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2D vikingSource VIKINGv20170715 Extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_1SerMag2DErr vikingSource VIKINGv20151230 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SerMag2DErr vikingSource VIKINGv20160406 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SerMag2DErr vikingSource VIKINGv20161202 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1SerMag2DErr vikingSource VIKINGv20170715 Error in extended source J_1 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_1Xi vikingSource VIKINGv20151230 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vikingSource VIKINGv20160406 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vikingSource VIKINGv20161202 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vikingSource VIKINGv20170715 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_1Xi vvvSource VVVDR5 Offset of J_1 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2AperMag1 vvvSource VVVDR5 Point source J_2 aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMag1Err vvvSource VVVDR5 Error in point source J_2 mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2AperMag3 vikingSource VIKINGv20151230 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vikingSource VIKINGv20160406 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vikingSource VIKINGv20161202 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vikingSource VIKINGv20170715 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMag3 vvvSource VVVDR5 Default point source J_2 aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMag3Err vikingSource VIKINGv20151230 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vikingSource VIKINGv20160406 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vikingSource VIKINGv20161202 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vikingSource VIKINGv20170715 Error in default point/extended source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag3Err vvvSource VVVDR5 Error in default point source J_2 mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2AperMag4 vikingSource VIKINGv20151230 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vikingSource VIKINGv20160406 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vikingSource VIKINGv20161202 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vikingSource VIKINGv20170715 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag4 vvvSource VVVDR5 Point source J_2 aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
j_2AperMag4Err vikingSource VIKINGv20151230 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vikingSource VIKINGv20160406 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vikingSource VIKINGv20161202 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vikingSource VIKINGv20170715 Error in point/extended source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag4Err vvvSource VVVDR5 Error in point source J_2 mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
j_2AperMag6 vikingSource VIKINGv20151230 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6 vikingSource VIKINGv20160406 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6 vikingSource VIKINGv20161202 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6 vikingSource VIKINGv20170715 Point source J_2 aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMag6Err vikingSource VIKINGv20151230 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag6Err vikingSource VIKINGv20160406 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag6Err vikingSource VIKINGv20161202 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMag6Err vikingSource VIKINGv20170715 Error in point/extended source J_2 mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20151230 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20160406 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20161202 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr3 vikingSource VIKINGv20170715 Default extended source J_2 aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20151230 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20160406 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20161202 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr4 vikingSource VIKINGv20170715 Extended source J_2 aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20151230 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20160406 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20161202 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AperMagNoAperCorr6 vikingSource VIKINGv20170715 Extended source J_2 aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
j_2AverageConf vikingSource VIKINGv20151230 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4   -0.9999995e9 stat.likelihood
j_2AverageConf vikingSource VIKINGv20160406 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4   -0.9999995e9 stat.likelihood
j_2AverageConf vikingSource VIKINGv20161202 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4   -0.9999995e9 stat.likelihood
j_2AverageConf vikingSource VIKINGv20170715 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4   -0.9999995e9 stat.likelihood
j_2AverageConf vvvSource VVVDR5 average confidence in 2 arcsec diameter default aperture (aper3) J_2 real 4   -0.9999995e9 stat.likelihood;em.IR.J
j_2Class vikingSource VIKINGv20151230 discrete image classification flag in J_2 smallint 2   -9999 src.class
j_2Class vikingSource VIKINGv20160406 discrete image classification flag in J_2 smallint 2   -9999 src.class
j_2Class vikingSource VIKINGv20161202 discrete image classification flag in J_2 smallint 2   -9999 src.class
j_2Class vikingSource VIKINGv20170715 discrete image classification flag in J_2 smallint 2   -9999 src.class
j_2Class vvvSource VVVDR5 discrete image classification flag in J_2 smallint 2   -9999 src.class;em.IR.J
j_2ClassStat vikingSource VIKINGv20151230 N(0,1) stellarness-of-profile statistic in J_2 real 4   -0.9999995e9 stat
j_2ClassStat vikingSource VIKINGv20160406 N(0,1) stellarness-of-profile statistic in J_2 real 4   -0.9999995e9 stat
j_2ClassStat vikingSource VIKINGv20161202 N(0,1) stellarness-of-profile statistic in J_2 real 4   -0.9999995e9 stat
j_2ClassStat vikingSource VIKINGv20170715 N(0,1) stellarness-of-profile statistic in J_2 real 4   -0.9999995e9 stat
j_2ClassStat vvvSource VVVDR5 S-Extractor classification statistic in J_2 real 4   -0.9999995e9 stat;em.IR.J
j_2Ell vikingSource VIKINGv20151230 1-b/a, where a/b=semi-major/minor axes in J_2 real 4   -0.9999995e9 src.ellipticity
j_2Ell vikingSource VIKINGv20160406 1-b/a, where a/b=semi-major/minor axes in J_2 real 4   -0.9999995e9 src.ellipticity
j_2Ell vikingSource VIKINGv20161202 1-b/a, where a/b=semi-major/minor axes in J_2 real 4   -0.9999995e9 src.ellipticity
j_2Ell vikingSource VIKINGv20170715 1-b/a, where a/b=semi-major/minor axes in J_2 real 4   -0.9999995e9 src.ellipticity
j_2Ell vvvSource VVVDR5 1-b/a, where a/b=semi-major/minor axes in J_2 real 4   -0.9999995e9 src.ellipticity;em.IR.J
j_2eNum vikingMergeLog VIKINGv20151230 the extension number of this J_2 frame tinyint 1     meta.number
j_2eNum vikingMergeLog VIKINGv20160406 the extension number of this J_2 frame tinyint 1     meta.number
j_2eNum vikingMergeLog VIKINGv20161202 the extension number of this J_2 frame tinyint 1     meta.number
j_2eNum vikingMergeLog VIKINGv20170715 the extension number of this J_2 frame tinyint 1     meta.number
j_2eNum vvvMergeLog VVVDR5 the extension number of this J_2 frame tinyint 1     meta.number;em.IR.J
j_2eNum vvvPsfDophotZYJHKsMergeLog VVVDR5 the extension number of this 2nd epoch J frame tinyint 1     meta.number;em.IR.J
j_2ErrBits vikingSource VIKINGv20151230 processing warning/error bitwise flags in J_2 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vikingSource VIKINGv20160406 processing warning/error bitwise flags in J_2 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vikingSource VIKINGv20161202 processing warning/error bitwise flags in J_2 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vikingSource VIKINGv20170715 processing warning/error bitwise flags in J_2 int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2ErrBits vvvSource VVVDR5 processing warning/error bitwise flags in J_2 int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
j_2Eta vikingSource VIKINGv20151230 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vikingSource VIKINGv20160406 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vikingSource VIKINGv20161202 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vikingSource VIKINGv20170715 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Eta vvvSource VVVDR5 Offset of J_2 detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Gausig vikingSource VIKINGv20151230 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vikingSource VIKINGv20160406 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vikingSource VIKINGv20161202 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vikingSource VIKINGv20170715 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param
j_2Gausig vvvSource VVVDR5 RMS of axes of ellipse fit in J_2 real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
j_2HlCorSMjRadAs vikingSource VIKINGv20151230 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2HlCorSMjRadAs vikingSource VIKINGv20160406 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2HlCorSMjRadAs vikingSource VIKINGv20161202 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2HlCorSMjRadAs vikingSource VIKINGv20170715 Seeing corrected half-light, semi-major axis in J_2 band real 4 arcsec -0.9999995e9 phys.angSize
j_2mfID vikingMergeLog VIKINGv20151230 the UID of the relevant J_2 multiframe bigint 8     meta.id;obs.field
j_2mfID vikingMergeLog VIKINGv20160406 the UID of the relevant J_2 multiframe bigint 8     meta.id;obs.field
j_2mfID vikingMergeLog VIKINGv20161202 the UID of the relevant J_2 multiframe bigint 8     meta.id;obs.field
j_2mfID vikingMergeLog VIKINGv20170715 the UID of the relevant J_2 multiframe bigint 8     meta.id;obs.field
j_2mfID vvvMergeLog VVVDR5 the UID of the relevant J_2 multiframe bigint 8     meta.id;obs.field;em.IR.J
j_2mfID vvvPsfDophotZYJHKsMergeLog VVVDR5 the UID of the relevant 2nd epoch J tile multiframe bigint 8     meta.id;obs.field;em.IR.J
j_2mh_2Pnt vvvSource VVVDR5 Point source colour J_2-H_2 (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_2mh_2PntErr vvvSource VVVDR5 Error on point source colour J_2-H_2 real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
j_2Mjd vikingSource VIKINGv20151230 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vikingSource VIKINGv20160406 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vikingSource VIKINGv20161202 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vikingSource VIKINGv20170715 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch
j_2Mjd vvvPsfDophotZYJHKsMergeLog VVVDR5 the MJD of the 2nd epoch J tile multiframe float 8     time;em.IR.J
j_2Mjd vvvSource VVVDR5 Modified Julian Day in J_2 band float 8 days -0.9999995e9 time.epoch;em.IR.J
j_2mrat twomass_scn TWOMASS J-band average 2nd image moment ratio. real 4     stat.fit.param
j_2mrat twomass_sixx2_scn TWOMASS J band average 2nd image moment ratio for scan real 4      
j_2PA vikingSource VIKINGv20151230 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vikingSource VIKINGv20160406 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vikingSource VIKINGv20161202 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vikingSource VIKINGv20170715 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng
j_2PA vvvSource VVVDR5 ellipse fit celestial orientation in J_2 real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
j_2PetroMag vikingSource VIKINGv20151230 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMag vikingSource VIKINGv20160406 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMag vikingSource VIKINGv20161202 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMag vikingSource VIKINGv20170715 Extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
j_2PetroMagErr vikingSource VIKINGv20151230 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PetroMagErr vikingSource VIKINGv20160406 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PetroMagErr vikingSource VIKINGv20161202 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PetroMagErr vikingSource VIKINGv20170715 Error in extended source J_2 mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2ppErrBits vikingSource VIKINGv20151230 additional WFAU post-processing error bits in J_2 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vikingSource VIKINGv20160406 additional WFAU post-processing error bits in J_2 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vikingSource VIKINGv20161202 additional WFAU post-processing error bits in J_2 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vikingSource VIKINGv20170715 additional WFAU post-processing error bits in J_2 int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
j_2ppErrBits vvvSource VVVDR5 additional WFAU post-processing error bits in J_2 int 4   0 meta.code;em.IR.J
j_2PsfMag vikingSource VIKINGv20151230 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMag vikingSource VIKINGv20160406 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMag vikingSource VIKINGv20161202 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMag vikingSource VIKINGv20170715 Point source profile-fitted J_2 mag real 4 mag -0.9999995e9 phot.mag
j_2PsfMagErr vikingSource VIKINGv20151230 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PsfMagErr vikingSource VIKINGv20160406 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PsfMagErr vikingSource VIKINGv20161202 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2PsfMagErr vikingSource VIKINGv20170715 Error in point source profile-fitted J_2 mag real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SeqNum vikingSource VIKINGv20151230 the running number of the J_2 detection int 4   -99999999 meta.number
j_2SeqNum vikingSource VIKINGv20160406 the running number of the J_2 detection int 4   -99999999 meta.number
j_2SeqNum vikingSource VIKINGv20161202 the running number of the J_2 detection int 4   -99999999 meta.number
j_2SeqNum vikingSource VIKINGv20170715 the running number of the J_2 detection int 4   -99999999 meta.number
j_2SeqNum vvvSource VVVDR5 the running number of the J_2 detection int 4   -99999999 meta.number;em.IR.J
j_2SerMag2D vikingSource VIKINGv20151230 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2D vikingSource VIKINGv20160406 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2D vikingSource VIKINGv20161202 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2D vikingSource VIKINGv20170715 Extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
j_2SerMag2DErr vikingSource VIKINGv20151230 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SerMag2DErr vikingSource VIKINGv20160406 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SerMag2DErr vikingSource VIKINGv20161202 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2SerMag2DErr vikingSource VIKINGv20170715 Error in extended source J_2 mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag
j_2Xi vikingSource VIKINGv20151230 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vikingSource VIKINGv20160406 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vikingSource VIKINGv20161202 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vikingSource VIKINGv20170715 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_2Xi vvvSource VVVDR5 Offset of J_2 detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
j_5sig_ba twomass_xsc TWOMASS J minor/major axis ratio fit to the 5-sigma isophote. real 4     phys.size.axisRatio
j_5sig_phi twomass_xsc TWOMASS J angle to 5-sigma major axis (E of N). smallint 2 degrees   stat.error
j_5surf twomass_xsc TWOMASS J central surface brightness (r<=5). real 4 mag   phot.mag.sb
j_ba twomass_xsc TWOMASS J minor/major axis ratio fit to the 3-sigma isophote. real 4     phys.size.axisRatio
j_back twomass_xsc TWOMASS J coadd median background. real 4     meta.code
j_bisym_chi twomass_xsc TWOMASS J bi-symmetric cross-correlation chi. real 4     stat.fit.param
j_bisym_rat twomass_xsc TWOMASS J bi-symmetric flux ratio. real 4     phot.flux;arith.ratio
j_bndg_amp twomass_xsc TWOMASS J banding maximum FT amplitude on this side of coadd. real 4 DN   stat.fit.param
j_bndg_per twomass_xsc TWOMASS J banding Fourier Transf. period on this side of coadd. int 4 arcsec   stat.fit.param
j_chif_ellf twomass_xsc TWOMASS J % chi-fraction for elliptical fit to 3-sig isophote. real 4     stat.fit.param
j_cmsig twomass_psc TWOMASS Corrected photometric uncertainty for the default J-band magnitude. real 4 mag J-band phot.flux
j_con_indx twomass_xsc TWOMASS J concentration index r_75%/r_25%. real 4     phys.size;arith.ratio
j_d_area twomass_xsc TWOMASS J 5-sigma to 3-sigma differential area. smallint 2     stat.fit.residual
j_flg_10 twomass_xsc TWOMASS J confusion flag for 10 arcsec circular ap. mag. smallint 2     meta.code
j_flg_15 twomass_xsc TWOMASS J confusion flag for 15 arcsec circular ap. mag. smallint 2     meta.code
j_flg_20 twomass_xsc TWOMASS J confusion flag for 20 arcsec circular ap. mag. smallint 2     meta.code
j_flg_25 twomass_xsc TWOMASS J confusion flag for 25 arcsec circular ap. mag. smallint 2     meta.code
j_flg_30 twomass_xsc TWOMASS J confusion flag for 30 arcsec circular ap. mag. smallint 2     meta.code
j_flg_40 twomass_xsc TWOMASS J confusion flag for 40 arcsec circular ap. mag. smallint 2     meta.code
j_flg_5 twomass_xsc TWOMASS J confusion flag for 5 arcsec circular ap. mag. smallint 2     meta.code
j_flg_50 twomass_xsc TWOMASS J confusion flag for 50 arcsec circular ap. mag. smallint 2     meta.code
j_flg_60 twomass_xsc TWOMASS J confusion flag for 60 arcsec circular ap. mag. smallint 2     meta.code
j_flg_7 twomass_sixx2_xsc TWOMASS J confusion flag for 7 arcsec circular ap. mag smallint 2      
j_flg_7 twomass_xsc TWOMASS J confusion flag for 7 arcsec circular ap. mag. smallint 2     meta.code
j_flg_70 twomass_xsc TWOMASS J confusion flag for 70 arcsec circular ap. mag. smallint 2     meta.code
j_flg_c twomass_xsc TWOMASS J confusion flag for Kron circular mag. smallint 2     meta.code
j_flg_e twomass_xsc TWOMASS J confusion flag for Kron elliptical mag. smallint 2     meta.code
j_flg_fc twomass_xsc TWOMASS J confusion flag for fiducial Kron circ. mag. smallint 2     meta.code
j_flg_fe twomass_xsc TWOMASS J confusion flag for fiducial Kron ell. mag. smallint 2     meta.code
j_flg_i20c twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. circ. mag. smallint 2     meta.code
j_flg_i20e twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. ell. mag. smallint 2     meta.code
j_flg_i21c twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. circ. mag. smallint 2     meta.code
j_flg_i21e twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. ell. mag. smallint 2     meta.code
j_flg_j21fc twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. fid. circ. mag. smallint 2     meta.code
j_flg_j21fe twomass_xsc TWOMASS J confusion flag for 21mag/sq." iso. fid. ell. mag. smallint 2     meta.code
j_flg_k20fc twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. fid. circ. mag. smallint 2     meta.code
j_flg_k20fe twomass_sixx2_xsc TWOMASS J confusion flag for 20mag/sq.″ iso. fid. ell. mag smallint 2      
j_flg_k20fe twomass_xsc TWOMASS J confusion flag for 20mag/sq." iso. fid. ell. mag. smallint 2     meta.code
j_h twomass_sixx2_psc TWOMASS The J-H color, computed from the J-band and H-band magnitudes (j_m and h_m, respectively) of the source. In cases where the first or second digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected. real 4      
j_k twomass_sixx2_psc TWOMASS The J-Ks color, computed from the J-band and Ks-band magnitudes (j_m and k_m, respectively) of the source. In cases where the first or third digit in rd_flg is equal to either "0", "4", "6", or "9", no color is computed because the photometry in one or both bands is of lower quality or the source is not detected. real 4      
j_m twomass_psc TWOMASS Default J-band magnitude real 4 mag   phot.flux
j_m twomass_sixx2_psc TWOMASS J selected "default" magnitude real 4 mag    
j_m_10 twomass_xsc TWOMASS J 10 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_15 twomass_xsc TWOMASS J 15 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_20 twomass_xsc TWOMASS J 20 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_25 twomass_xsc TWOMASS J 25 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_2mass allwise_sc WISE 2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is "null". float 8 mag    
j_m_2mass wise_allskysc WISE 2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source.
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is default.
real 4 mag -0.9999995e9  
j_m_2mass wise_prelimsc WISE 2MASS J-band magnitude or magnitude upper limit of the associated 2MASS PSC source
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band magnitude entry is default
real 4 mag -0.9999995e9  
j_m_30 twomass_xsc TWOMASS J 30 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_40 twomass_xsc TWOMASS J 40 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_5 twomass_xsc TWOMASS J 5 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_50 twomass_xsc TWOMASS J 50 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_60 twomass_xsc TWOMASS J 60 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_7 twomass_sixx2_xsc TWOMASS J 7 arcsec radius circular aperture magnitude real 4 mag    
j_m_7 twomass_xsc TWOMASS J 7 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_70 twomass_xsc TWOMASS J 70 arcsec radius circular aperture magnitude. real 4 mag   phot.flux
j_m_c twomass_xsc TWOMASS J Kron circular aperture magnitude. real 4 mag   phot.flux
j_m_e twomass_xsc TWOMASS J Kron elliptical aperture magnitude. real 4 mag   phot.flux
j_m_ext twomass_sixx2_xsc TWOMASS J mag from fit extrapolation real 4 mag    
j_m_ext twomass_xsc TWOMASS J mag from fit extrapolation. real 4 mag   phot.flux
j_m_fc twomass_xsc TWOMASS J fiducial Kron circular magnitude. real 4 mag   phot.flux
j_m_fe twomass_xsc TWOMASS J fiducial Kron ell. mag aperture magnitude. real 4 mag   phot.flux
j_m_i20c twomass_xsc TWOMASS J 20mag/sq." isophotal circular ap. magnitude. real 4 mag   phot.flux
j_m_i20e twomass_xsc TWOMASS J 20mag/sq." isophotal elliptical ap. magnitude. real 4 mag   phot.flux
j_m_i21c twomass_xsc TWOMASS J 21mag/sq." isophotal circular ap. magnitude. real 4 mag   phot.flux
j_m_i21e twomass_xsc TWOMASS J 21mag/sq." isophotal elliptical ap. magnitude. real 4 mag   phot.flux
j_m_j21fc twomass_xsc TWOMASS J 21mag/sq." isophotal fiducial circ. ap. mag. real 4 mag   phot.flux
j_m_j21fe twomass_xsc TWOMASS J 21mag/sq." isophotal fiducial ell. ap. magnitude. real 4 mag   phot.flux
j_m_k20fc twomass_xsc TWOMASS J 20mag/sq." isophotal fiducial circ. ap. mag. real 4 mag   phot.flux
J_M_K20FE twomass SIXDF J 20mag/sq." isophotal fiducial ell. ap. magnitude real 4 mag    
j_m_k20fe twomass_sixx2_xsc TWOMASS J 20mag/sq.″ isophotal fiducial ell. ap. magnitude real 4 mag    
j_m_k20fe twomass_xsc TWOMASS J 20mag/sq." isophotal fiducial ell. ap. magnitude. real 4 mag   phot.flux
j_m_stdap twomass_psc TWOMASS J-band "standard" aperture magnitude. real 4 mag   phot.flux
j_m_sys twomass_xsc TWOMASS J system photometry magnitude. real 4 mag   phot.flux
j_mnsurfb_eff twomass_xsc TWOMASS J mean surface brightness at the half-light radius. real 4 mag   phot.mag.sb
j_msig twomass_sixx2_psc TWOMASS J "default" mag uncertainty real 4 mag    
j_msig_10 twomass_xsc TWOMASS J 1-sigma uncertainty in 10 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_15 twomass_xsc TWOMASS J 1-sigma uncertainty in 15 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_20 twomass_xsc TWOMASS J 1-sigma uncertainty in 20 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_25 twomass_xsc TWOMASS J 1-sigma uncertainty in 25 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_2mass allwise_sc WISE 2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is "null". float 8 mag    
j_msig_2mass wise_allskysc WISE 2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source.
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is default.
real 4 mag -0.9999995e9  
j_msig_2mass wise_prelimsc WISE 2MASS J-band corrected photometric uncertainty of the associated 2MASS PSC source
This column is default if there is no associated 2MASS PSC source or if the 2MASS PSC J-band uncertainty entry is default
real 4 mag -0.9999995e9  
j_msig_30 twomass_xsc TWOMASS J 1-sigma uncertainty in 30 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_40 twomass_xsc TWOMASS J 1-sigma uncertainty in 40 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_5 twomass_xsc TWOMASS J 1-sigma uncertainty in 5 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_50 twomass_xsc TWOMASS J 1-sigma uncertainty in 50 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_60 twomass_xsc TWOMASS J 1-sigma uncertainty in 60 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_7 twomass_sixx2_xsc TWOMASS J 1-sigma uncertainty in 7 arcsec circular ap. mag real 4 mag    
j_msig_7 twomass_xsc TWOMASS J 1-sigma uncertainty in 7 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_70 twomass_xsc TWOMASS J 1-sigma uncertainty in 70 arcsec circular ap. mag. real 4 mag   stat.error
j_msig_c twomass_xsc TWOMASS J 1-sigma uncertainty in Kron circular mag. real 4 mag   stat.error
j_msig_e twomass_xsc TWOMASS J 1-sigma uncertainty in Kron elliptical mag. real 4 mag   stat.error
j_msig_ext twomass_sixx2_xsc TWOMASS J 1-sigma uncertainty in mag from fit extrapolation real 4 mag    
j_msig_ext twomass_xsc TWOMASS J 1-sigma uncertainty in mag from fit extrapolation. real 4 mag   stat.error
j_msig_fc twomass_xsc TWOMASS J 1-sigma uncertainty in fiducial Kron circ. mag. real 4 mag   stat.error
j_msig_fe twomass_xsc TWOMASS J 1-sigma uncertainty in fiducial Kron ell. mag. real 4 mag   stat.error
j_msig_i20c twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso. circ. mag. real 4 mag   stat.error
j_msig_i20e twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso. ell. mag. real 4 mag   stat.error
j_msig_i21c twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso. circ. mag. real 4 mag   stat.error
j_msig_i21e twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso. ell. mag. real 4 mag   stat.error
j_msig_j21fc twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso.fid.circ.mag. real 4 mag   stat.error
j_msig_j21fe twomass_xsc TWOMASS J 1-sigma uncertainty in 21mag/sq." iso.fid.ell.mag. real 4 mag   stat.error
j_msig_k20fc twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso.fid.circ. mag. real 4 mag   stat.error
j_msig_k20fe twomass_xsc TWOMASS J 1-sigma uncertainty in 20mag/sq." iso.fid.ell.mag. real 4 mag   stat.error
j_msig_stdap twomass_psc TWOMASS Uncertainty in the J-band standard aperture magnitude. real 4 mag   phot.flux
j_msig_sys twomass_xsc TWOMASS J 1-sigma uncertainty in system photometry mag. real 4 mag   stat.error
j_msigcom twomass_psc TWOMASS Combined, or total photometric uncertainty for the default J-band magnitude. real 4 mag J-band phot.flux
j_msigcom twomass_sixx2_psc TWOMASS combined (total) J band photometric uncertainty real 4 mag    
j_msnr10 twomass_scn TWOMASS The estimated J-band magnitude at which SNR=10 is achieved for this scan. real 4 mag   phot.flux
j_msnr10 twomass_sixx2_scn TWOMASS J mag at which SNR=10 is achieved, from j_psp and j_zp_ap real 4 mag    
j_n_snr10 twomass_scn TWOMASS Number of point sources at J-band with SNR>10 (instrumental mag <=15.8) int 4     meta.number
j_n_snr10 twomass_sixx2_scn TWOMASS number of J point sources with SNR>10 (instrumental m<=15.8) int 4      
j_pchi twomass_xsc TWOMASS J chi^2 of fit to rad. profile (LCSB: alpha scale len). real 4     stat.fit.param
j_peak twomass_xsc TWOMASS J peak pixel brightness. real 4 mag   phot.mag.sb
j_perc_darea twomass_xsc TWOMASS J 5-sigma to 3-sigma percent area change. smallint 2     FIT_PARAM
j_phi twomass_xsc TWOMASS J angle to 3-sigma major axis (E of N). smallint 2 degrees   pos.posAng
j_psfchi twomass_psc TWOMASS Reduced chi-squared goodness-of-fit value for the J-band profile-fit photometry made on the 1.3 s "Read_2" exposures. real 4     stat.fit.param
j_psp twomass_scn TWOMASS J-band photometric sensitivity paramater (PSP). real 4     instr.sensitivity
j_psp twomass_sixx2_scn TWOMASS J photometric sensitivity param: j_shape_avg*(j_fbg_avg^.29) real 4      
j_pts_noise twomass_scn TWOMASS Base-10 logarithm of the mode of the noise distribution for all point source detections in the scan, where the noise is estimated from the measured J-band photometric errors and is expressed in units of mJy. real 4     instr.det.noise
j_pts_noise twomass_sixx2_scn TWOMASS log10 of J band modal point src noise estimate real 4 logmJy    
j_r_c twomass_xsc TWOMASS J Kron circular aperture radius. real 4 arcsec   phys.angSize;src
j_r_e twomass_xsc TWOMASS J Kron elliptical aperture semi-major axis. real 4 arcsec   phys.angSize;src
j_r_eff twomass_xsc TWOMASS J half-light (integrated half-flux point) radius. real 4 arcsec   phys.angSize;src
j_r_i20c twomass_xsc TWOMASS J 20mag/sq." isophotal circular aperture radius. real 4 arcsec   phys.angSize;src
j_r_i20e twomass_xsc TWOMASS J 20mag/sq." isophotal elliptical ap. semi-major axis. real 4 arcsec   phys.angSize;src
j_r_i21c twomass_xsc TWOMASS J 21mag/sq." isophotal circular aperture radius. real 4 arcsec   phys.angSize;src
j_r_i21e twomass_xsc TWOMASS J 21mag/sq." isophotal elliptical ap. semi-major axis. real 4 arcsec   phys.angSize;src
j_resid_ann twomass_xsc TWOMASS J residual annulus background median. real 4 DN   meta.code
j_sc_1mm twomass_xsc TWOMASS J 1st moment (score) (LCSB: super blk 2,4,8 SNR). real 4     meta.code
j_sc_2mm twomass_xsc TWOMASS J 2nd moment (score) (LCSB: SNRMAX - super SNR max). real 4     meta.code
j_sc_msh twomass_xsc TWOMASS J median shape score. real 4     meta.code
j_sc_mxdn twomass_xsc TWOMASS J mxdn (score) (LCSB: BSNR - block/smoothed SNR). real 4     meta.code
j_sc_r1 twomass_xsc TWOMASS J r1 (score). real 4     meta.code
j_sc_r23 twomass_xsc TWOMASS J r23 (score) (LCSB: TSNR - integrated SNR for r=15). real 4     meta.code
j_sc_sh twomass_xsc TWOMASS J shape (score). real 4     meta.code
j_sc_vint twomass_xsc TWOMASS J vint (score). real 4     meta.code
j_sc_wsh twomass_xsc TWOMASS J wsh (score) (LCSB: PSNR - peak raw SNR). real 4     meta.code
j_seetrack twomass_xsc TWOMASS J band seetracking score. real 4     meta.code
j_sh0 twomass_xsc TWOMASS J ridge shape (LCSB: BSNR limit). real 4     FIT_PARAM
j_shape_avg twomass_scn TWOMASS J-band average seeing shape for scan. real 4     instr.obsty.seeing
j_shape_avg twomass_sixx2_scn TWOMASS J band average seeing shape for scan real 4      
j_shape_rms twomass_scn TWOMASS RMS-error of J-band average seeing shape. real 4     instr.obsty.seeing
j_shape_rms twomass_sixx2_scn TWOMASS rms of J band avg seeing shape for scan real 4      
j_sig_sh0 twomass_xsc TWOMASS J ridge shape sigma (LCSB: B2SNR limit). real 4     FIT_PARAM
j_snr twomass_psc TWOMASS J-band "scan" signal-to-noise ratio. real 4 mag   instr.det.noise
j_snr twomass_sixx2_psc TWOMASS J band "scan" signal-to-noise ratio real 4      
j_subst2 twomass_xsc TWOMASS J residual background #2 (score). real 4     meta.code
j_zp_ap twomass_scn TWOMASS Photometric zero-point for J-band aperture photometry. real 4 mag   phot.mag;arith.zp
j_zp_ap twomass_sixx2_scn TWOMASS J band ap. calibration photometric zero-point for scan real 4 mag    
jAmpl vmcCepheidVariables VMCDR4 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20160311 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20160822 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20170109 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20170411 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20171101 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20180702 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20181120 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20191212 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20210708 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20230816 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmpl vmcCepheidVariables VMCv20240226 Peak-to-Peak amplitude in J band {catalogue TType keyword: A(J)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCDR4 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20160311 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20160822 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20170109 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20170411 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20171101 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20180702 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20181120 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20191212 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20210708 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20230816 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAmplErr vmcCepheidVariables VMCv20240226 Error in Peak-to-Peak amplitude in J band {catalogue TType keyword: e_A(J)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.J
jAperJky3 ultravistaSourceRemeasurement ULTRAVISTADR4 Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJky3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJky3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source J aperture corrected (2.0 arcsec aperture diameter) calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJky3Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source J (2.0 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky4 ultravistaSourceRemeasurement ULTRAVISTADR4 Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky4Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky6 ultravistaSourceRemeasurement ULTRAVISTADR4 Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJky6Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJky6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
jAperJkyNoAperCorr3 ultravistaSourceRemeasurement ULTRAVISTADR4 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr4 ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr6 ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperJkyNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
jAperLup3 ultravistaSourceRemeasurement ULTRAVISTADR4 Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLup3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLup3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source J aperture corrected (2.0 arcsec aperture diameter) luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLup3Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source J (2.0 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup4 ultravistaSourceRemeasurement ULTRAVISTADR4 Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup4Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in point/extended source J (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup6 ultravistaSourceRemeasurement ULTRAVISTADR4 Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
jAperLup6Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in point/extended source J (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLup6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
jAperLupNoAperCorr3 ultravistaSourceRemeasurement ULTRAVISTADR4 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr4 ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr6 ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperLupNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
jAperMag1 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCDR5 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag1 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20191212 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20210708 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20230816 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcSynopticSource VMCv20240226 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vmcdeepSynopticSource VMCDEEPv20230713 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vvvSource VVVDR1 Extended source J aperture corrected mag (0.7 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag1 vvvSource VVVDR5 Point source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1 vvvSource VVVv20100531 Extended source J aperture corrected mag (0.7 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag1 vvvSource VVVv20110718 Extended source J aperture corrected mag (0.7 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag1 vvvSource, vvvSynopticSource VVVDR2 Extended source J aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCDR1 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCDR2 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCDR3 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag1Err vmcSynopticSource VMCDR4 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCDR5 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20110816 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20110909 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20120126 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20121128 Error in extended source J mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20130304 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20130805 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vmcSynopticSource VMCv20140428 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20140903 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag1Err vmcSynopticSource VMCv20150309 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag1Err vmcSynopticSource VMCv20151218 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20160311 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20160822 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20170109 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20170411 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20171101 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20180702 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20181120 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20191212 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20210708 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20230816 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcSynopticSource VMCv20240226 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vmcdeepSynopticSource VMCDEEPv20230713 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vvvSource VVVDR1 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vvvSource VVVDR5 Error in point source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag1Err vvvSource VVVv20100531 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vvvSource VVVv20110718 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag1Err vvvSource, vvvSynopticSource VVVDR2 Error in extended source J mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCDR5 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20191212 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20210708 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20230816 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcSynopticSource VMCv20240226 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vmcdeepSynopticSource VMCDEEPv20230713 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2 vvvSynopticSource VVVDR1 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag2 vvvSynopticSource VVVDR2 Extended source J aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCDR1 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCDR2 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCDR3 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag2Err vmcSynopticSource VMCDR4 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCDR5 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20110816 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20110909 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20120126 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20121128 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20130304 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20130805 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vmcSynopticSource VMCv20140428 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20140903 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag2Err vmcSynopticSource VMCv20150309 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag2Err vmcSynopticSource VMCv20151218 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20160311 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20160822 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20170109 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20170411 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20171101 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20180702 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20181120 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20191212 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20210708 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20230816 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcSynopticSource VMCv20240226 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vmcdeepSynopticSource VMCDEEPv20230713 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag2Err vvvSynopticSource VVVDR1 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag2Err vvvSynopticSource VVVDR2 Error in extended source J mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3 ultravistaSource ULTRAVISTADR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 ultravistaSourceRemeasurement ULTRAVISTADR4 Default point source J aperture corrected (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSDR1 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSDR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSDR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSDR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSDR5 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSDR6 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20120926 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSv20130417 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vhsSource VHSv20140409 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20150108 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20160114 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20160507 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20170630 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20180419 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vhsSource VHSv20201209 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 videoSource VIDEODR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 videoSource VIDEODR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 videoSource VIDEODR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 videoSource VIDEODR5 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 videoSource VIDEOv20100513 Default point/extended source J mag, no aperture correction applied
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 videoSource VIDEOv20111208 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGDR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGDR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGDR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20110714 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGv20111019 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGv20130417 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingSource VIKINGv20140402 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20150421 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20151230 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20160406 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20161202 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingSource VIKINGv20170715 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source J aperture corrected (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source J aperture corrected (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCDR1 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCDR2 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCDR3 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCDR4 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCDR5 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20110816 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20110909 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20120126 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20121128 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20130304 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSource VMCv20130805 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20140428 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20140903 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20150309 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20151218 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20160311 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20160822 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20170109 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20170411 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20171101 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20180702 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20181120 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20191212 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20210708 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20230816 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSource VMCv20240226 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCDR1 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCDR2 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCDR3 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCDR4 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCDR5 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20110816 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20110909 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20120126 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20121128 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20130304 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vmcSynopticSource VMCv20130805 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20140428 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20140903 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20150309 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20151218 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20160311 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20160822 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20170109 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20170411 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20171101 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20180702 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20181120 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20191212 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20210708 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20230816 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcSynopticSource VMCv20240226 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcdeepSource VMCDEEPv20230713 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vmcdeepSynopticSource VMCDEEPv20230713 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vvvSource VVVDR1 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vvvSource VVVDR2 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vvvSource VVVDR5 Default point source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vvvSource VVVv20100531 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vvvSource VVVv20110718 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMag3 vvvSynopticSource VVVDR1 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag3 vvvSynopticSource VVVDR2 Default point/extended source J aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag3 vvvVivaCatalogue VVVDR5 J magnitude using aperture corrected mag (2.0 arcsec aperture diameter, from VVVDR4 1st epoch JHKs contemporaneous OB) {catalogue TType keyword: jAperMag3} real 4 mag -9.999995e8  
jAperMag3Err ultravistaSource ULTRAVISTADR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSDR1 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSDR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSDR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vhsSource VHSDR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vhsSource VHSDR5 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSDR6 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20120926 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSv20130417 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vhsSource VHSv20140409 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vhsSource VHSv20150108 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vhsSource VHSv20160114 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20160507 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20170630 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20180419 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vhsSource VHSv20201209 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err videoSource VIDEODR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err videoSource VIDEODR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err videoSource VIDEODR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err videoSource VIDEODR5 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err videoSource VIDEOv20100513 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err videoSource VIDEOv20111208 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGDR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGDR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGDR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vikingSource VIKINGv20110714 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20111019 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20130417 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20140402 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingSource VIKINGv20150421 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vikingSource VIKINGv20151230 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingSource VIKINGv20160406 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingSource VIKINGv20161202 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingSource VIKINGv20170715 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source J (2.0 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCDR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCDR3 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vmcSource VMCDR4 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCDR5 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20110816 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20110909 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20120126 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20121128 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20130304 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20130805 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcSource VMCv20140428 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag3Err vmcSource VMCv20140903 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vmcSource VMCv20150309 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag3Err vmcSource VMCv20151218 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20160311 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20160822 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20170109 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20170411 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20171101 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20180702 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20181120 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20191212 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20210708 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20230816 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource VMCv20240226 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vmcSource, vmcSynopticSource VMCDR1 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vvvSource VVVDR2 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vvvSource VVVDR5 Error in default point source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag3Err vvvSource VVVv20100531 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vvvSource VVVv20110718 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vvvSource, vvvSynopticSource VVVDR1 Error in default point/extended source J mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag3Err vvvVivaCatalogue VVVDR5 Error in default point source J mag, from VVVDR4 {catalogue TType keyword: jAperMag3Err} real 4 mag -9.999995e8  
jAperMag4 ultravistaSource ULTRAVISTADR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 ultravistaSourceRemeasurement ULTRAVISTADR4 Point source J aperture corrected (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSDR1 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSDR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSDR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSDR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSDR5 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSDR6 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20120926 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSv20130417 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vhsSource VHSv20140409 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20150108 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20160114 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20160507 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20170630 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20180419 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vhsSource VHSv20201209 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 videoSource VIDEODR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 videoSource VIDEODR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 videoSource VIDEODR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 videoSource VIDEODR5 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 videoSource VIDEOv20100513 Extended source J mag, no aperture correction applied real 4 mag -0.9999995e9 phot.mag
jAperMag4 videoSource VIDEOv20111208 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGDR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGDR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGDR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20110714 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGv20111019 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGv20130417 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingSource VIKINGv20140402 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20150421 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20151230 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20160406 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20161202 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingSource VIKINGv20170715 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCDR1 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCDR2 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCDR3 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCDR4 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCDR5 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20110816 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20110909 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20120126 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20121128 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20130304 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSource VMCv20130805 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20140428 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20140903 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20150309 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20151218 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20160311 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20160822 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20170109 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20170411 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20171101 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20180702 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20181120 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20191212 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20210708 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20230816 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSource VMCv20240226 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCDR5 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20191212 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20210708 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20230816 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcSynopticSource VMCv20240226 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcdeepSource VMCDEEPv20230713 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vmcdeepSynopticSource VMCDEEPv20230713 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vvvSource VVVDR2 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vvvSource VVVDR5 Point source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag4 vvvSource VVVv20100531 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vvvSource VVVv20110718 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4 vvvSource, vvvSynopticSource VVVDR1 Extended source J aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag4Err ultravistaSource ULTRAVISTADR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in point/extended source J (2.8 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSDR1 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSDR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSDR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vhsSource VHSDR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vhsSource VHSDR5 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSDR6 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20120926 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSv20130417 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vhsSource VHSv20140409 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vhsSource VHSv20150108 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vhsSource VHSv20160114 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20160507 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20170630 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20180419 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vhsSource VHSv20201209 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err videoSource VIDEODR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err videoSource VIDEODR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err videoSource VIDEODR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err videoSource VIDEODR5 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err videoSource VIDEOv20100513 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err videoSource VIDEOv20111208 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGDR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGDR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGDR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vikingSource VIKINGv20110714 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20111019 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20130417 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20140402 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingSource VIKINGv20150421 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vikingSource VIKINGv20151230 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingSource VIKINGv20160406 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingSource VIKINGv20161202 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingSource VIKINGv20170715 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (2.8 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (2.8 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCDR1 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCDR2 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCDR3 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSource VMCDR4 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCDR5 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20110816 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20110909 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20120126 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20121128 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20130304 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20130805 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSource VMCv20140428 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vmcSource VMCv20140903 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSource VMCv20150309 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSource VMCv20151218 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20160311 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20160822 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20170109 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20170411 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20171101 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20180702 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20181120 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20191212 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20210708 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20230816 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSource VMCv20240226 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCDR1 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCDR2 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCDR3 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSynopticSource VMCDR4 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCDR5 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20110816 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20110909 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20120126 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20121128 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20130304 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20130805 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vmcSynopticSource VMCv20140428 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20140903 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSynopticSource VMCv20150309 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag4Err vmcSynopticSource VMCv20151218 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20160311 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20160822 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20170109 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20170411 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20171101 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20180702 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20181120 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20191212 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20210708 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20230816 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcSynopticSource VMCv20240226 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcdeepSource VMCDEEPv20230713 Error in point/extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vmcdeepSynopticSource VMCDEEPv20230713 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vvvSource VVVDR2 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vvvSource VVVDR5 Error in point source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag4Err vvvSource VVVv20100531 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vvvSource VVVv20110718 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag4Err vvvSource, vvvSynopticSource VVVDR1 Error in extended source J mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5 vmcSynopticSource VMCDR1 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCDR2 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCDR3 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCDR4 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCDR5 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20110816 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20110909 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20120126 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20121128 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20130304 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vmcSynopticSource VMCv20130805 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20140428 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20140903 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20150309 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20151218 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20160311 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20160822 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20170109 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20170411 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20171101 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20180702 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20181120 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20191212 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20210708 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20230816 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcSynopticSource VMCv20240226 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vmcdeepSynopticSource VMCDEEPv20230713 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5 vvvSynopticSource VVVDR1 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag5 vvvSynopticSource VVVDR2 Extended source J aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCDR1 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCDR2 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCDR3 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag5Err vmcSynopticSource VMCDR4 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCDR5 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20110816 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20110909 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20120126 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20121128 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20130304 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20130805 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vmcSynopticSource VMCv20140428 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20140903 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag5Err vmcSynopticSource VMCv20150309 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag5Err vmcSynopticSource VMCv20151218 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20160311 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20160822 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20170109 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20170411 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20171101 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20180702 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20181120 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20191212 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20210708 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20230816 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcSynopticSource VMCv20240226 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vmcdeepSynopticSource VMCDEEPv20230713 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag5Err vvvSynopticSource VVVDR1 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag5Err vvvSynopticSource VVVDR2 Error in extended source J mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6 ultravistaSource ULTRAVISTADR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 ultravistaSourceRemeasurement ULTRAVISTADR4 Point source J aperture corrected (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSDR1 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSDR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSDR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSDR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSDR5 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSDR6 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20120926 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSv20130417 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vhsSource VHSv20140409 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20150108 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20160114 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20160507 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20170630 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20180419 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vhsSource VHSv20201209 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 videoSource VIDEODR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 videoSource VIDEODR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 videoSource VIDEODR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 videoSource VIDEODR5 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 videoSource VIDEOv20100513 Extended source J mag, no aperture correction applied real 4 mag -0.9999995e9 phot.mag
jAperMag6 videoSource VIDEOv20111208 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGDR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGDR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGDR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20110714 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGv20111019 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGv20130417 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingSource VIKINGv20140402 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20150421 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20151230 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20160406 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20161202 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingSource VIKINGv20170715 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source J aperture corrected (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source J aperture corrected (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCDR1 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCDR2 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCDR3 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCDR4 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCDR5 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20110816 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20110909 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20120126 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20121128 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20130304 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMag6 vmcSource VMCv20130805 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20140428 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20140903 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20150309 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20151218 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20160311 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20160822 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20170109 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20170411 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20171101 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20180702 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20181120 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20191212 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20210708 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20230816 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcSource VMCv20240226 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6 vmcdeepSource VMCDEEPv20230713 Point source J aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMag6Err ultravistaSource ULTRAVISTADR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err ultravistaSourceRemeasurement ULTRAVISTADR4 Error in point/extended source J (5.7 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSDR1 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSDR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSDR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vhsSource VHSDR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vhsSource VHSDR5 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSDR6 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20120926 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSv20130417 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vhsSource VHSv20140409 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vhsSource VHSv20150108 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vhsSource VHSv20160114 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20160507 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20170630 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20180419 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vhsSource VHSv20201209 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err videoSource VIDEODR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err videoSource VIDEODR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err videoSource VIDEODR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err videoSource VIDEODR5 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err videoSource VIDEOv20100513 Error in extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err videoSource VIDEOv20111208 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGDR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGDR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGDR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vikingSource VIKINGv20110714 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20111019 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20130417 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20140402 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingSource VIKINGv20150421 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vikingSource VIKINGv20151230 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingSource VIKINGv20160406 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingSource VIKINGv20161202 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingSource VIKINGv20170715 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source J (5.7 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source J (5.7 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCDR1 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCDR2 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCDR3 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vmcSource VMCDR4 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCDR5 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20110816 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20110909 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20120126 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20121128 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20130304 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20130805 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
jAperMag6Err vmcSource VMCv20140428 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J
jAperMag6Err vmcSource VMCv20140903 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vmcSource VMCv20150309 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jAperMag6Err vmcSource VMCv20151218 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20160311 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20160822 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20170109 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20170411 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20171101 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20180702 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20181120 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20191212 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20210708 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20230816 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcSource VMCv20240226 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMag6Err vmcdeepSource VMCDEEPv20230713 Error in point/extended source J mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jAperMagNoAperCorr3 ultravistaSource ULTRAVISTADR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 ultravistaSourceRemeasurement ULTRAVISTADR4 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSDR1 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSDR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSDR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSDR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSDR5 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSDR6 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20120926 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSv20130417 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vhsSource VHSv20140409 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20150108 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20160114 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20160507 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20170630 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20180419 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vhsSource VHSv20201209 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 videoSource VIDEODR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 videoSource VIDEODR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 videoSource VIDEODR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 videoSource VIDEODR5 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 videoSource VIDEOv20111208 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGDR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGDR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGDR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20110714 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGv20111019 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGv20130417 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingSource VIKINGv20140402 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20150421 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20151230 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20160406 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20161202 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingSource VIKINGv20170715 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source J (2.0 arcsec aperture diameter, but no aperture correction applied) aperture magnitude
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCDR1 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCDR2 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCDR3 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCDR4 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCDR5 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20110816 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20110909 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20120126 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20121128 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20130304 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr3 vmcSource VMCv20130805 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20140428 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20140903 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20150309 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20151218 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20160311 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20160822 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20170109 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20170411 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20171101 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20180702 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20181120 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20191212 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20210708 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20230816 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcSource VMCv20240226 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr3 vmcdeepSource VMCDEEPv20230713 Default extended source J aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 ultravistaSource ULTRAVISTADR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSDR1 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSDR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSDR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSDR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSDR5 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSDR6 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20120926 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSv20130417 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vhsSource VHSv20140409 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20150108 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20160114 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20160507 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20170630 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20180419 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vhsSource VHSv20201209 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 videoSource VIDEODR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 videoSource VIDEODR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 videoSource VIDEODR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 videoSource VIDEODR5 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 videoSource VIDEOv20111208 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGDR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGDR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGDR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20110714 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGv20111019 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGv20130417 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingSource VIKINGv20140402 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20150421 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20151230 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20160406 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20161202 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingSource VIKINGv20170715 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCDR1 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCDR2 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCDR3 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCDR4 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCDR5 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20110816 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20110909 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20120126 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20121128 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20130304 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr4 vmcSource VMCv20130805 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20140428 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20140903 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20150309 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20151218 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20160311 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20160822 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20170109 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20170411 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20171101 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20180702 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20181120 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20191212 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20210708 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20230816 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcSource VMCv20240226 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr4 vmcdeepSource VMCDEEPv20230713 Extended source J aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 ultravistaSource ULTRAVISTADR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSDR1 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSDR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSDR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSDR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSDR5 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSDR6 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20120926 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSv20130417 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vhsSource VHSv20140409 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20150108 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20160114 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20160507 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20170630 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20180419 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vhsSource VHSv20201209 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 videoSource VIDEODR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 videoSource VIDEODR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 videoSource VIDEODR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 videoSource VIDEODR5 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 videoSource VIDEOv20111208 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGDR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGDR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGDR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20110714 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGv20111019 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGv20130417 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingSource VIKINGv20140402 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20150421 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20151230 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20160406 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20161202 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingSource VIKINGv20170715 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source J (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCDR1 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCDR2 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCDR3 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCDR4 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCDR5 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20110816 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20110909 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20120126 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20121128 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20130304 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
jAperMagNoAperCorr6 vmcSource VMCv20130805 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20140428 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20140903 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20150309 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20151218 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20160311 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20160822 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20170109 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20170411 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20171101 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20180702 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20181120 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20191212 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20210708 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20230816 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcSource VMCv20240226 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jAperMagNoAperCorr6 vmcdeepSource VMCDEEPv20230713 Extended source J aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jaStratAst ultravistaVarFrameSetInfo ULTRAVISTADR4 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCDR5 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20191212 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20210708 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20230816 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcVarFrameSetInfo VMCv20240226 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vmcdeepVarFrameSetInfo VMCDEEPv20230713 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vvvVarFrameSetInfo VVVDR5 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratAst vvvVarFrameSetInfo VVVv20100531 Strateva parameter, a, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jaStratPht ultravistaMapLcVarFrameSetInfo ULTRAVISTADR4 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht ultravistaVarFrameSetInfo ULTRAVISTADR4 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCDR5 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20191212 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20210708 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20230816 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcVarFrameSetInfo VMCv20240226 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vmcdeepVarFrameSetInfo VMCDEEPv20230713 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vvvVarFrameSetInfo VVVDR5 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jaStratPht vvvVarFrameSetInfo VVVv20100531 Strateva parameter, a, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jAverageConf ultravistaSourceRemeasurement ULTRAVISTADR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vhsSource VHSDR1 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vhsSource VHSDR2 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vhsSource VHSDR3 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSDR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSDR5 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSDR6 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20120926 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 stat.likelihood;em.IR.NIR
jAverageConf vhsSource VHSv20130417 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vhsSource VHSv20140409 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20150108 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20160114 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20160507 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20170630 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20180419 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vhsSource VHSv20201209 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGDR2 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vikingSource VIKINGDR3 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 stat.likelihood;em.IR.NIR
jAverageConf vikingSource VIKINGDR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20110714 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vikingSource VIKINGv20111019 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vikingSource VIKINGv20130417 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vikingSource VIKINGv20140402 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vikingSource VIKINGv20150421 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20151230 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20160406 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20161202 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingSource VIKINGv20170715 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCDR2 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCDR3 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCDR4 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCDR5 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20110816 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vmcSource VMCv20110909 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vmcSource VMCv20120126 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vmcSource VMCv20121128 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCv20130304 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCv20130805 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vmcSource VMCv20140428 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20140903 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20150309 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20151218 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20160311 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20160822 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20170109 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20170411 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20171101 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20180702 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20181120 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20191212 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20210708 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20230816 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource VMCv20240226 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vmcSource, vmcSynopticSource VMCDR1 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 meta.code
jAverageConf vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vvvSource VVVDR2 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
jAverageConf vvvSource VVVDR5 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -0.9999995e9 stat.likelihood;em.IR.J
jAverageConf vvvSource, vvvSynopticSource VVVDR1 average confidence in 2 arcsec diameter default aperture (aper3) J real 4   -99999999 stat.likelihood;em.IR.NIR
jbestAper ultravistaMapLcVariability ULTRAVISTADR4 Best aperture (1-3) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper ultravistaVariability ULTRAVISTADR4 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR2 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR3 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR4 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEODR5 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEOv20100513 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper videoVariability VIDEOv20111208 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR2 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR3 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGDR4 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20110714 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20111019 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20130417 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20140402 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20150421 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20151230 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20160406 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20161202 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vikingVariability VIKINGv20170715 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR1 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR2 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR3 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR4 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCDR5 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20110816 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20110909 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20120126 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20121128 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20130304 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20130805 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.NIR
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20140428 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20140903 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20150309 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20151218 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20160311 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20160822 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20170109 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20170411 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20171101 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20180702 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20181120 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20191212 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20210708 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20230816 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcVariability VMCv20240226 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vmcdeepVariability VMCDEEPv20230713 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vvvVariability VVVDR5 Best aperture (1-6) for photometric statistics in the J band int 4   -9999 meta.code.class;em.IR.J
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbestAper vvvVariability VVVv20100531 Best aperture (1-6) for photometric statistics in the J band int 4   -9999  
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
jbStratAst ultravistaVarFrameSetInfo ULTRAVISTADR4 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCDR5 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20191212 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20210708 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20230816 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcVarFrameSetInfo VMCv20240226 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vmcdeepVarFrameSetInfo VMCDEEPv20230713 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vvvVarFrameSetInfo VVVDR5 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratAst vvvVarFrameSetInfo VVVv20100531 Strateva parameter, b, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jbStratPht ultravistaMapLcVarFrameSetInfo ULTRAVISTADR4 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht ultravistaVarFrameSetInfo ULTRAVISTADR4 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCDR5 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20191212 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20210708 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20230816 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcVarFrameSetInfo VMCv20240226 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vmcdeepVarFrameSetInfo VMCDEEPv20230713 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vvvVarFrameSetInfo VVVDR5 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jbStratPht vvvVarFrameSetInfo VVVv20100531 Strateva parameter, b, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqAst ultravistaVarFrameSetInfo ULTRAVISTADR4 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEODR2 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEODR3 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEODR4 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEODR5 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEOv20100513 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst videoVarFrameSetInfo VIDEOv20111208 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGDR2 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGDR3 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGDR4 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20110714 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20111019 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20130417 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20140402 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20150421 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20151230 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20160406 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20161202 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vikingVarFrameSetInfo VIKINGv20170715 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR1 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR2 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR3 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR4 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCDR5 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20110816 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20110909 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20120126 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20121128 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20130304 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20130805 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20140428 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20140903 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20150309 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20151218 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20160311 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20160822 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20170109 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20170411 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20171101 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20180702 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20181120 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20191212 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20210708 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20230816 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcVarFrameSetInfo VMCv20240226 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vmcdeepVarFrameSetInfo VMCDEEPv20230713 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vvvVarFrameSetInfo VVVDR5 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqAst vvvVarFrameSetInfo VVVv20100531 Goodness of fit of Strateva function to astrometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jchiSqpd ultravistaMapLcVariability ULTRAVISTADR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd ultravistaVariability ULTRAVISTADR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEODR2 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEODR3 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEODR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEODR5 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEOv20100513 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd videoVariability VIDEOv20111208 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGDR2 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGDR3 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGDR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20110714 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20111019 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20130417 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20140402 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20150421 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20151230 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20160406 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20161202 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vikingVariability VIKINGv20170715 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR1 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR2 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR3 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCDR5 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20110816 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20110909 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20120126 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20121128 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20130304 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20130805 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20140428 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20140903 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20150309 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20151218 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20160311 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20160822 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20170109 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20170411 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20171101 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20180702 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20181120 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20191212 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20210708 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20230816 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcVariability VMCv20240226 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vmcdeepVariability VMCDEEPv20230713 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vvvVariability VVVDR5 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqpd vvvVariability VVVv20100531 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jchiSqPht ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo ULTRAVISTADR4 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEODR2 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEODR3 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEODR4 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEODR5 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEOv20100513 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht videoVarFrameSetInfo VIDEOv20111208 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGDR2 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGDR3 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGDR4 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20110714 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20111019 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20130417 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20140402 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20150421 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20151230 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20160406 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20161202 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vikingVarFrameSetInfo VIKINGv20170715 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR1 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR2 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR3 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR4 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCDR5 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20110816 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20110909 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20120126 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20121128 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20130304 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20130805 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20140428 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20140903 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20150309 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20151218 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20160311 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20160822 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20170109 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20170411 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20171101 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20180702 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20181120 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20191212 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20210708 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20230816 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcVarFrameSetInfo VMCv20240226 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vmcdeepVarFrameSetInfo VMCDEEPv20230713 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vvvVarFrameSetInfo VVVDR5 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9 stat.fit.goodness;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jchiSqPht vvvVarFrameSetInfo VVVv20100531 Goodness of fit of Strateva function to photometric data in J band real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
Jclass vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR5 VVV DR4 J morphological classification. 1 = galaxy,0 = noise,-1 = stellar,-2 = probably stellar,-3 = probable galaxy,-7 = bad pixel within 2" aperture,-9 = saturated {catalogue TType keyword: Jclass} int 4   -99999999  
jClass ultravistaSource ULTRAVISTADR4 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass ultravistaSourceRemeasurement ULTRAVISTADR4 discrete image classification flag in J smallint 2   -9999 src.class
jClass vhsSource VHSDR2 discrete image classification flag in J smallint 2   -9999 src.class
jClass vhsSource VHSDR3 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSDR4 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSDR5 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSDR6 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSv20120926 discrete image classification flag in J smallint 2   -9999 src.class
jClass vhsSource VHSv20130417 discrete image classification flag in J smallint 2   -9999 src.class
jClass vhsSource VHSv20140409 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSv20150108 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSv20160114 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSv20160507 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSv20170630 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSv20180419 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource VHSv20201209 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vhsSource, vhsSourceRemeasurement VHSDR1 discrete image classification flag in J smallint 2   -9999 src.class
jClass videoSource VIDEODR2 discrete image classification flag in J smallint 2   -9999 src.class
jClass videoSource VIDEODR3 discrete image classification flag in J smallint 2   -9999 src.class
jClass videoSource VIDEODR4 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass videoSource VIDEODR5 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass videoSource VIDEOv20111208 discrete image classification flag in J smallint 2   -9999 src.class
jClass videoSource, videoSourceRemeasurement VIDEOv20100513 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingSource VIKINGDR2 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingSource VIKINGDR3 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingSource VIKINGDR4 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20111019 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingSource VIKINGv20130417 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingSource VIKINGv20140402 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingSource VIKINGv20150421 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20151230 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20160406 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20161202 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vikingSource VIKINGv20170715 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vikingSource, vikingSourceRemeasurement VIKINGv20110714 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 discrete image classification flag in J smallint 2   -9999 src.class
jClass vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource VMCDR2 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource VMCDR3 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCDR4 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCDR5 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20110909 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource VMCv20120126 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource VMCv20121128 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource VMCv20130304 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource VMCv20130805 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource VMCv20140428 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20140903 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20150309 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20151218 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20160311 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20160822 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20170109 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20170411 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20171101 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20180702 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20181120 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20191212 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20210708 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20230816 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource VMCv20240226 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vmcSource, vmcSourceRemeasurement VMCv20110816 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcSource, vmcSynopticSource VMCDR1 discrete image classification flag in J smallint 2   -9999 src.class
jClass vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vvvSource VVVDR2 discrete image classification flag in J smallint 2   -9999 src.class
jClass vvvSource VVVDR5 discrete image classification flag in J smallint 2   -9999 src.class;em.IR.J
jClass vvvSource VVVv20110718 discrete image classification flag in J smallint 2   -9999 src.class
jClass vvvSource, vvvSourceRemeasurement VVVv20100531 discrete image classification flag in J smallint 2   -9999 src.class
jClass vvvSource, vvvSynopticSource VVVDR1 discrete image classification flag in J smallint 2   -9999 src.class
jClassStat ultravistaSource ULTRAVISTADR4 S-Extractor classification statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat ultravistaSourceRemeasurement ULTRAVISTADR4 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSDR2 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSDR3 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSDR4 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSDR5 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSDR6 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20120926 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSv20130417 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vhsSource VHSv20140409 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20150108 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20160114 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20160507 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20170630 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20180419 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource VHSv20201209 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vhsSource, vhsSourceRemeasurement VHSDR1 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEODR2 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEODR3 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEODR4 S-Extractor classification statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat videoSource VIDEODR5 S-Extractor classification statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat videoSource VIDEOv20100513 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSource VIDEOv20111208 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat videoSourceRemeasurement VIDEOv20100513 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGDR2 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGDR3 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGDR4 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20111019 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGv20130417 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGv20140402 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingSource VIKINGv20150421 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20151230 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20160406 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20161202 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource VIKINGv20170715 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vikingSource, vikingSourceRemeasurement VIKINGv20110714 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCDR2 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCDR3 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCDR4 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCDR5 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20110909 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20120126 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20121128 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20130304 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20130805 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource VMCv20140428 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20140903 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20150309 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20151218 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20160311 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20160822 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20170109 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20170411 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20171101 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20180702 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20181120 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20191212 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20210708 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20230816 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource VMCv20240226 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vmcSource, vmcSourceRemeasurement VMCv20110816 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcSource, vmcSynopticSource VMCDR1 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vvvSource VVVDR1 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSource VVVDR2 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSource VVVDR5 S-Extractor classification statistic in J real 4   -0.9999995e9 stat;em.IR.J
jClassStat vvvSource VVVv20100531 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSource VVVv20110718 S-Extractor classification statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSourceRemeasurement VVVv20100531 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSourceRemeasurement VVVv20110718 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSynopticSource VVVDR1 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jClassStat vvvSynopticSource VVVDR2 N(0,1) stellarness-of-profile statistic in J real 4   -0.9999995e9 stat
jCorr twompzPhotoz TWOMPZ J 20mag/sq." isophotal fiducial ell. ap. magnitude with Galactic dust correction {image primary HDU keyword: Jcorr} real 4 mag -0.9999995e9 phot.mag;em.IR.J
jCorrErr twompzPhotoz TWOMPZ J 1-sigma uncertainty in 20mag/sq." aperture {image primary HDU keyword: j_msig_k20fe} real 4 mag -0.9999995e9  
jcStratAst ultravistaVarFrameSetInfo ULTRAVISTADR4 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCDR5 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20191212 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20210708 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20230816 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcVarFrameSetInfo VMCv20240226 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vmcdeepVarFrameSetInfo VMCDEEPv20230713 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vvvVarFrameSetInfo VVVDR5 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratAst vvvVarFrameSetInfo VVVv20100531 Strateva parameter, c, in fit to astrometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jcStratPht ultravistaMapLcVarFrameSetInfo ULTRAVISTADR4 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht ultravistaVarFrameSetInfo ULTRAVISTADR4 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGDR2 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGDR3 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGDR4 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20111019 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20130417 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20140402 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20150421 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20151230 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20160406 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20161202 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vikingVarFrameSetInfo VIKINGv20170715 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCDR5 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20191212 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20210708 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20230816 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcVarFrameSetInfo VMCv20240226 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vmcdeepVarFrameSetInfo VMCDEEPv20230713 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vvvVarFrameSetInfo VVVDR5 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in J band. real 4   -0.9999995e9 stat.fit.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jcStratPht vvvVarFrameSetInfo VVVv20100531 Strateva parameter, c, in fit to photometric rms vs magnitude in J band, see Sesar et al. 2007. real 4   -0.9999995e9  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jdate twomass_psc TWOMASS The Julian Date of the source measurement accurate to +-30 seconds. float 8 Julian days   time.epoch
jdate twomass_scn TWOMASS Julian Date at beginning of scan. float 8 Julian days   time.epoch
jdate twomass_sixx2_psc TWOMASS julian date of source measurement to +/- 30 sec float 8 jdate    
jdate twomass_sixx2_scn TWOMASS Julian date beginning UT of scan data float 8 jdate    
jdate twomass_xsc TWOMASS Julian date of the source measurement accurate to +-3 minutes. float 8 Julian days   time.epoch
jDeblend vhsSourceRemeasurement VHSDR1 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
jDeblend videoSource, videoSourceRemeasurement VIDEOv20100513 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
jDeblend vikingSourceRemeasurement VIKINGv20110714 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
jDeblend vikingSourceRemeasurement VIKINGv20111019 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
jDeblend vmcSourceRemeasurement VMCv20110816 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
jDeblend vmcSourceRemeasurement VMCv20110909 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
jDeblend vvvSource VVVv20110718 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
jDeblend vvvSource, vvvSourceRemeasurement VVVv20100531 placeholder flag indicating parent/child relation in J int 4   -99999999 meta.code
Jell vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR5 Ellipticity of the DR4 J detection. {catalogue TType keyword: Jell} real 4   -999999500.0  
jEll ultravistaSource ULTRAVISTADR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll ultravistaSourceRemeasurement ULTRAVISTADR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticty
jEll vhsSource VHSDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vhsSource VHSDR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSDR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSDR5 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSDR6 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20120926 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vhsSource VHSv20130417 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vhsSource VHSv20140409 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20150108 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20160114 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20160507 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20170630 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20180419 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource VHSv20201209 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vhsSource, vhsSourceRemeasurement VHSDR1 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource VIDEODR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll videoSource VIDEODR5 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll videoSource VIDEOv20111208 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll videoSource, videoSourceRemeasurement VIDEOv20100513 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGDR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20111019 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20130417 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20140402 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingSource VIKINGv20150421 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20151230 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20160406 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20161202 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource VIKINGv20170715 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vikingSource, vikingSourceRemeasurement VIKINGv20110714 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCDR3 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCDR4 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCDR5 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20110909 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20120126 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20121128 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20130304 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20130805 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource VMCv20140428 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20140903 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20150309 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20151218 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20160311 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20160822 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20170109 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20170411 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20171101 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20180702 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20181120 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20191212 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20210708 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20230816 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource VMCv20240226 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vmcSource, vmcSourceRemeasurement VMCv20110816 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcSource, vmcSynopticSource VMCDR1 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vvvSource VVVDR2 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vvvSource VVVDR5 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity;em.IR.J
jEll vvvSource VVVv20110718 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vvvSource, vvvSourceRemeasurement VVVv20100531 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jEll vvvSource, vvvSynopticSource VVVDR1 1-b/a, where a/b=semi-major/minor axes in J real 4   -0.9999995e9 src.ellipticity
jeNum ultravistaMergeLog, ultravistaRemeasMergeLog ULTRAVISTADR4 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSDR1 the extension number of this J frame tinyint 1     meta.number
jeNum vhsMergeLog VHSDR2 the extension number of this J frame tinyint 1     meta.number
jeNum vhsMergeLog VHSDR3 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSDR4 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSDR5 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSDR6 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSv20120926 the extension number of this J frame tinyint 1     meta.number
jeNum vhsMergeLog VHSv20130417 the extension number of this J frame tinyint 1     meta.number
jeNum vhsMergeLog VHSv20140409 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSv20150108 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSv20160114 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSv20160507 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSv20170630 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSv20180419 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vhsMergeLog VHSv20201209 the extension number of this J frame tinyint 1     meta.id;em.IR.J
jeNum videoMergeLog VIDEODR2 the extension number of this J frame tinyint 1     meta.number
jeNum videoMergeLog VIDEODR3 the extension number of this J frame tinyint 1     meta.number
jeNum videoMergeLog VIDEODR4 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum videoMergeLog VIDEODR5 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum videoMergeLog VIDEOv20100513 the extension number of this J frame tinyint 1     meta.number
jeNum videoMergeLog VIDEOv20111208 the extension number of this J frame tinyint 1     meta.number
jeNum vikingMergeLog VIKINGDR2 the extension number of this J frame tinyint 1     meta.number
jeNum vikingMergeLog VIKINGDR3 the extension number of this J frame tinyint 1     meta.number
jeNum vikingMergeLog VIKINGDR4 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20110714 the extension number of this J frame tinyint 1     meta.number
jeNum vikingMergeLog VIKINGv20111019 the extension number of this J frame tinyint 1     meta.number
jeNum vikingMergeLog VIKINGv20130417 the extension number of this J frame tinyint 1     meta.number
jeNum vikingMergeLog VIKINGv20140402 the extension number of this J frame tinyint 1     meta.number
jeNum vikingMergeLog VIKINGv20150421 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20151230 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20160406 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20161202 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vikingMergeLog VIKINGv20170715 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20160909 the extension number of this J frame tinyint 1     meta.number
jeNum vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20170124 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCDR2 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCDR3 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCDR4 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCDR5 the extension number of this J frame tinyint 1     meta.id;em.IR.J
jeNum vmcMergeLog VMCv20110816 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCv20110909 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCv20120126 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCv20121128 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCv20130304 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCv20130805 the extension number of this J frame tinyint 1     meta.number
jeNum vmcMergeLog VMCv20140428 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20140903 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20150309 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20151218 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20160311 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20160822 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20170109 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20170411 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20171101 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20180702 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20181120 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vmcMergeLog VMCv20191212 the extension number of this J frame tinyint 1     meta.id;em.IR.J
jeNum vmcMergeLog VMCv20210708 the extension number of this J frame tinyint 1     meta.id;em.IR.J
jeNum vmcMergeLog VMCv20230816 the extension number of this J frame tinyint 1     meta.id;em.IR.J
jeNum vmcMergeLog VMCv20240226 the extension number of this J frame tinyint 1     meta.id;em.IR.J
jeNum vmcMergeLog, vmcSynopticMergeLog VMCDR1 the extension number of this J frame tinyint 1     meta.number
jeNum vmcdeepMergeLog, vmcdeepSynopticMergeLog VMCDEEPv20230713 the extension number of this J frame tinyint 1     meta.id;em.IR.J
jeNum vvvMergeLog VVVDR2 the extension number of this J frame tinyint 1     meta.number
jeNum vvvMergeLog VVVv20100531 the extension number of this J frame tinyint 1     meta.number
jeNum vvvMergeLog VVVv20110718 the extension number of this J frame tinyint 1     meta.number
jeNum vvvMergeLog, vvvPsfDaophotJKsMergeLog VVVDR5 the extension number of this J frame tinyint 1     meta.number;em.IR.J
jeNum vvvMergeLog, vvvSynopticMergeLog VVVDR1 the extension number of this J frame tinyint 1     meta.number
jErrBits ultravistaSource ULTRAVISTADR4 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

jErrBits ultravistaSourceRemeasurement ULTRAVISTADR4 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR1 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR2 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR3 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR4 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR5 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSDR6 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20120926 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20130417 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20140409 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20150108 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20160114 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20160507 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20170630 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20180419 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSource VHSv20201209 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vhsSourceRemeasurement VHSDR1 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jErrBits videoSource VIDEODR2 processing warning/error bitwise flags in J int 4   -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

jErrBits videoSource VIDEODR3 processing warning/error bitwise flags in J int 4   -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

jErrBits videoSource VIDEODR4 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

jErrBits videoSource VIDEODR5 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

jErrBits videoSource VIDEOv20100513 processing warning/error bitwise flags in J int 4   -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

jErrBits videoSource VIDEOv20111208 processing warning/error bitwise flags in J int 4   -99999999 meta.code
This uses the FLAGS attribute in SE. The individual bit flags that this can be decomposed into are as follows:
Bit FlagMeaning
1The object has neighbours, bright enough and close enough to significantly bias the MAG_AUTO photometry or bad pixels (more than 10% of photometry affected).
2The object was originally blended with another
4At least one pixel is saturated (or very close to)
8The object is truncated (too close to an image boundary)
16Object's aperture data are incomplete or corrupted
32Object's isophotal data are imcomplete or corrupted. This is an old flag inherited from SE v1.0, and is kept for compatability reasons. It doesn't have any consequence for the extracted parameters.
64Memory overflow occurred during deblending
128Memory overflow occurred during extraction

jErrBits videoSourceRemeasurement VIDEOv20100513 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jErrBits vikingSource VIKINGDR2 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGDR3 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGDR4 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20110714 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20111019 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20130417 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20140402 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20150421 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20151230 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20160406 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20161202 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSource VIKINGv20170715 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingSourceRemeasurement VIKINGv20110714 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jErrBits vikingSourceRemeasurement VIKINGv20111019 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCDR2 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCDR3 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCDR4 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCDR5 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20110816 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20110909 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20120126 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20121128 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20130304 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20130805 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20140428 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20140903 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20150309 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20151218 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20160311 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20160822 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20170109 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20170411 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20171101 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20180702 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20181120 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20191212 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20210708 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20230816 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource VMCv20240226 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSource, vmcSynopticSource VMCDR1 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vmcSourceRemeasurement VMCv20110816 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jErrBits vmcSourceRemeasurement VMCv20110909 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jErrBits vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vvvSource VVVDR2 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vvvSource VVVDR5 processing warning/error bitwise flags in J int 4   -99999999 meta.code;em.IR.J
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vvvSource VVVv20100531 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vvvSource VVVv20110718 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vvvSource, vvvSynopticSource VVVDR1 processing warning/error bitwise flags in J int 4   -99999999 meta.code
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
jErrBits vvvSourceRemeasurement VVVv20100531 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jErrBits vvvSourceRemeasurement VVVv20110718 processing warning/error bitwise flags in J int 4   -99999999 meta.code
jEta ultravistaSource ULTRAVISTADR4 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSDR1 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSDR2 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSDR3 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSDR4 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSDR5 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSDR6 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20120926 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20130417 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20140409 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20150108 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20160114 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20160507 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20170630 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20180419 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vhsSource VHSv20201209 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta videoSource VIDEODR2 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta videoSource VIDEODR3 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta videoSource VIDEODR4 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta videoSource VIDEODR5 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta videoSource VIDEOv20100513 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta videoSource VIDEOv20111208 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGDR2 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGDR3 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGDR4 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20110714 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20111019 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20130417 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20140402 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20150421 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20151230 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20160406 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20161202 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vikingSource VIKINGv20170715 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCDR2 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCDR3 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCDR4 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCDR5 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20110816 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20110909 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20120126 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20121128 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20130304 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20130805 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20140428 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20140903 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20150309 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20151218 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20160311 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20160822 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20170109 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20170411 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20171101 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20180702 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20181120 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20191212 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20210708 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20230816 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource VMCv20240226 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcSource, vmcSynopticSource VMCDR1 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vvvSource VVVDR2 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vvvSource VVVDR5 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vvvSource VVVv20100531 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vvvSource VVVv20110718 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jEta vvvSource, vvvSynopticSource VVVDR1 Offset of J detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jexpML ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo ULTRAVISTADR4 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML videoVarFrameSetInfo VIDEODR2 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML videoVarFrameSetInfo VIDEODR3 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9 phot.mag;stat.max;em.IR.NIR
jexpML videoVarFrameSetInfo VIDEODR4 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML videoVarFrameSetInfo VIDEODR5 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML videoVarFrameSetInfo VIDEOv20100513 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML videoVarFrameSetInfo VIDEOv20111208 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vikingVarFrameSetInfo VIKINGDR2 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vikingVarFrameSetInfo VIKINGDR3 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
jexpML vikingVarFrameSetInfo VIKINGDR4 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vikingVarFrameSetInfo VIKINGv20110714 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vikingVarFrameSetInfo VIKINGv20111019 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vikingVarFrameSetInfo VIKINGv20130417 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
jexpML vikingVarFrameSetInfo VIKINGv20140402 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max;em.IR.NIR
jexpML vikingVarFrameSetInfo VIKINGv20150421 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vikingVarFrameSetInfo VIKINGv20151230 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vikingVarFrameSetInfo VIKINGv20160406 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vikingVarFrameSetInfo VIKINGv20161202 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vikingVarFrameSetInfo VIKINGv20170715 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCDR1 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vmcVarFrameSetInfo VMCDR2 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max;em.IR.NIR
jexpML vmcVarFrameSetInfo VMCDR3 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCDR4 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCDR5 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20110816 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vmcVarFrameSetInfo VMCv20110909 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vmcVarFrameSetInfo VMCv20120126 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jexpML vmcVarFrameSetInfo VMCv20121128 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
jexpML vmcVarFrameSetInfo VMCv20130304 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
jexpML vmcVarFrameSetInfo VMCv20130805 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max;em.IR.NIR
jexpML vmcVarFrameSetInfo VMCv20140428 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20140903 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20150309 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20151218 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20160311 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20160822 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20170109 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20170411 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20171101 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20180702 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20181120 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20191212 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20210708 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20230816 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcVarFrameSetInfo VMCv20240226 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vmcdeepVarFrameSetInfo VMCDEEPv20230713 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vvvVarFrameSetInfo VVVDR5 Expected magnitude limit of frameSet in this in J band. real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
jexpML vvvVarFrameSetInfo VVVv20100531 Expected magnitude limit of frameSet in this in J band. real 4   -0.9999995e9  
jExpRms ultravistaMapLcVariability ULTRAVISTADR4 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms ultravistaVariability ULTRAVISTADR4 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms videoVariability VIDEODR2 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms videoVariability VIDEODR3 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms videoVariability VIDEODR4 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms videoVariability VIDEODR5 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms videoVariability VIDEOv20100513 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms videoVariability VIDEOv20111208 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGDR2 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGDR3 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGDR4 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20110714 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20111019 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20130417 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20140402 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20150421 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20151230 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20160406 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20161202 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vikingVariability VIKINGv20170715 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCDR1 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCDR2 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCDR3 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCDR4 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCDR5 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20110816 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20110909 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20120126 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20121128 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20130304 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20130805 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20140428 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20140903 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20150309 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20151218 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20160311 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20160822 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20170109 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20170411 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20171101 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20180702 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20181120 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20191212 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20210708 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20230816 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcVariability VMCv20240226 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vmcdeepVariability VMCDEEPv20230713 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vvvVariability VVVDR5 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jExpRms vvvVariability VVVv20100531 Rms calculated from polynomial fit to modal RMS as a function of magnitude in J band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jGausig ultravistaSource ULTRAVISTADR4 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig ultravistaSourceRemeasurement ULTRAVISTADR4 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vhsSource VHSDR2 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vhsSource VHSDR3 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSDR4 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSDR5 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSDR6 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSv20120926 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vhsSource VHSv20130417 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vhsSource VHSv20140409 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSv20150108 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSv20160114 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSv20160507 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSv20170630 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSv20180419 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource VHSv20201209 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vhsSource, vhsSourceRemeasurement VHSDR1 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig videoSource VIDEODR2 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig videoSource VIDEODR3 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig videoSource VIDEODR4 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig videoSource VIDEODR5 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig videoSource VIDEOv20111208 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig videoSource, videoSourceRemeasurement VIDEOv20100513 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingSource VIKINGDR2 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingSource VIKINGDR3 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingSource VIKINGDR4 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vikingSource VIKINGv20111019 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingSource VIKINGv20130417 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingSource VIKINGv20140402 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingSource VIKINGv20150421 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vikingSource VIKINGv20151230 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vikingSource VIKINGv20160406 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vikingSource VIKINGv20161202 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vikingSource VIKINGv20170715 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vikingSource, vikingSourceRemeasurement VIKINGv20110714 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource VMCDR2 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource VMCDR3 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCDR4 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCDR5 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20110909 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource VMCv20120126 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource VMCv20121128 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource VMCv20130304 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource VMCv20130805 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource VMCv20140428 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20140903 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20150309 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20151218 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20160311 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20160822 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20170109 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20170411 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20171101 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20180702 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20181120 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20191212 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20210708 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20230816 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource VMCv20240226 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vmcSource, vmcSourceRemeasurement VMCv20110816 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcSource, vmcSynopticSource VMCDR1 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vvvSource VVVDR2 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vvvSource VVVDR5 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param;em.IR.J
jGausig vvvSource VVVv20110718 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vvvSource, vvvSourceRemeasurement VVVv20100531 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jGausig vvvSource, vvvSynopticSource VVVDR1 RMS of axes of ellipse fit in J real 4 pixels -0.9999995e9 src.morph.param
jHalfRad ultravistaSource ULTRAVISTADR4 SExtractor half-light radius in J band real 4 pixels -0.9999995e9 phys.angSize;em.IR.J
jHalfRad videoSource VIDEODR4 SExtractor half-light radius in J band real 4 pixels -0.9999995e9 phys.angSize;em.IR.J
jHalfRad videoSource VIDEODR5 SExtractor half-light radius in J band real 4 pixels -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs ultravistaSource ULTRAVISTADR4 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs ultravistaSourceRemeasurement ULTRAVISTADR4 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vhsSource VHSDR1 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vhsSource VHSDR2 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vhsSource VHSDR3 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSDR4 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSDR5 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSDR6 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20120926 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vhsSource VHSv20130417 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vhsSource VHSv20140409 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20150108 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20160114 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20160507 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20170630 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20180419 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vhsSource VHSv20201209 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs videoSource VIDEODR2 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs videoSource VIDEODR3 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs videoSource VIDEODR4 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs videoSource VIDEODR5 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs videoSource VIDEOv20100513 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs videoSource VIDEOv20111208 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGDR2 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGDR3 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vikingSource VIKINGDR4 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vikingSource VIKINGv20110714 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGv20111019 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;src
jHlCorSMjRadAs vikingSource VIKINGv20130417 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vikingSource VIKINGv20140402 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize
jHlCorSMjRadAs vikingSource VIKINGv20150421 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vikingSource VIKINGv20151230 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vikingSource VIKINGv20160406 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vikingSource VIKINGv20161202 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jHlCorSMjRadAs vikingSource VIKINGv20170715 Seeing corrected half-light, semi-major axis in J band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.J
jIntRms ultravistaMapLcVariability ULTRAVISTADR4 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms ultravistaVariability ULTRAVISTADR4 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms videoVariability VIDEODR2 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms videoVariability VIDEODR3 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms videoVariability VIDEODR4 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms videoVariability VIDEODR5 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms videoVariability VIDEOv20100513 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms videoVariability VIDEOv20111208 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGDR2 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGDR3 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGDR4 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20110714 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20111019 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20130417 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20140402 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20150421 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20151230 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20160406 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20161202 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vikingVariability VIKINGv20170715 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCDR1 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCDR2 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCDR3 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCDR4 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCDR5 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20110816 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20110909 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20120126 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20121128 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20130304 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20130805 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20140428 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20140903 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20150309 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20151218 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20160311 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20160822 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20170109 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20170411 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20171101 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20180702 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20181120 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20191212 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20210708 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20230816 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcVariability VMCv20240226 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vmcdeepVariability VMCDEEPv20230713 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vvvVariability VVVDR5 Intrinsic rms in J-band real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jIntRms vvvVariability VVVv20100531 Intrinsic rms in J-band real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jisDefAst ultravistaVarFrameSetInfo ULTRAVISTADR4 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst videoVarFrameSetInfo VIDEODR2 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst videoVarFrameSetInfo VIDEODR3 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst videoVarFrameSetInfo VIDEODR4 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst videoVarFrameSetInfo VIDEODR5 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst videoVarFrameSetInfo VIDEOv20111208 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst vikingVarFrameSetInfo VIKINGDR2 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst vikingVarFrameSetInfo VIKINGDR3 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst vikingVarFrameSetInfo VIKINGDR4 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vikingVarFrameSetInfo VIKINGv20111019 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst vikingVarFrameSetInfo VIKINGv20130417 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst vikingVarFrameSetInfo VIKINGv20140402 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst vikingVarFrameSetInfo VIKINGv20150421 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vikingVarFrameSetInfo VIKINGv20151230 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vikingVarFrameSetInfo VIKINGv20160406 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vikingVarFrameSetInfo VIKINGv20161202 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vikingVarFrameSetInfo VIKINGv20170715 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCDR1 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst vmcVarFrameSetInfo VMCDR2 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst vmcVarFrameSetInfo VMCDR3 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCDR4 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCDR5 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20110816 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst vmcVarFrameSetInfo VMCv20110909 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst vmcVarFrameSetInfo VMCv20120126 Use a default model for the astrometric noise in J band. tinyint 1   0  
jisDefAst vmcVarFrameSetInfo VMCv20121128 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst vmcVarFrameSetInfo VMCv20130304 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst vmcVarFrameSetInfo VMCv20130805 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefAst vmcVarFrameSetInfo VMCv20140428 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20140903 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20150309 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20151218 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20160311 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20160822 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20170109 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20170411 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20171101 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20180702 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20181120 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20191212 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20210708 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20230816 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcVarFrameSetInfo VMCv20240226 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vmcdeepVarFrameSetInfo VMCDEEPv20230713 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefAst vvvVarFrameSetInfo VVVDR5 Use a default model for the astrometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo ULTRAVISTADR4 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht videoVarFrameSetInfo VIDEODR2 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht videoVarFrameSetInfo VIDEODR3 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht videoVarFrameSetInfo VIDEODR4 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht videoVarFrameSetInfo VIDEODR5 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht videoVarFrameSetInfo VIDEOv20111208 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht vikingVarFrameSetInfo VIKINGDR2 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht vikingVarFrameSetInfo VIKINGDR3 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht vikingVarFrameSetInfo VIKINGDR4 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vikingVarFrameSetInfo VIKINGv20111019 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht vikingVarFrameSetInfo VIKINGv20130417 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht vikingVarFrameSetInfo VIKINGv20140402 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht vikingVarFrameSetInfo VIKINGv20150421 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vikingVarFrameSetInfo VIKINGv20151230 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vikingVarFrameSetInfo VIKINGv20160406 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vikingVarFrameSetInfo VIKINGv20161202 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vikingVarFrameSetInfo VIKINGv20170715 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCDR1 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht vmcVarFrameSetInfo VMCDR2 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht vmcVarFrameSetInfo VMCDR3 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCDR4 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCDR5 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20110816 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht vmcVarFrameSetInfo VMCv20110909 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht vmcVarFrameSetInfo VMCv20120126 Use a default model for the photometric noise in J band. tinyint 1   0  
jisDefPht vmcVarFrameSetInfo VMCv20121128 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht vmcVarFrameSetInfo VMCv20130304 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht vmcVarFrameSetInfo VMCv20130805 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.NIR
jisDefPht vmcVarFrameSetInfo VMCv20140428 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20140903 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20150309 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20151218 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20160311 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20160822 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20170109 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20170411 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20171101 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20180702 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20181120 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20191212 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20210708 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20230816 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcVarFrameSetInfo VMCv20240226 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vmcdeepVarFrameSetInfo VMCDEEPv20230713 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jisDefPht vvvVarFrameSetInfo VVVDR5 Use a default model for the photometric noise in J band. tinyint 1   0 meta.code;em.IR.J
jIsMeas ultravistaSourceRemeasurement ULTRAVISTADR4 Is pass band J measured? 0 no, 1 yes tinyint 1   0 meta.code
jIsMeas vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Is pass band J measured? 0 no, 1 yes tinyint 1   0 meta.code
jIsMeas vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Is pass band J measured? 0 no, 1 yes tinyint 1   0 meta.code
jitterID Multiframe SHARKSv20210222 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe SHARKSv20210421 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe ULTRAVISTADR4 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSDR1 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSDR2 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSDR3 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSDR4 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSDR5 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSDR6 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20120926 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20130417 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20140409 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20150108 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20160114 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20160507 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20170630 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20180419 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VHSv20201209 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIDEODR2 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIDEODR3 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIDEODR4 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIDEODR5 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIDEOv20100513 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIDEOv20111208 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGDR2 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGDR3 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGDR4 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20110714 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20111019 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20130417 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20140402 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20150421 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20151230 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20160406 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20161202 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VIKINGv20170715 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCDEEPv20230713 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCDR1 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCDR2 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCDR3 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCDR4 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCDR5 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20110816 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20110909 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20120126 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20121128 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20130304 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20130805 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20140428 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20140903 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20150309 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20151218 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20160311 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20160822 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20170109 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20170411 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20171101 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20180702 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20181120 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20191212 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20210708 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20230816 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VMCv20240226 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VVVDR1 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VVVDR2 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VVVDR5 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VVVXDR1 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VVVv20100531 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID Multiframe VVVv20110718 Sequence number of jitter {image primary HDU keyword: JITTER_I} smallint 2   -9999  
jitterID sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Sequence number of jitter smallint 2   -9999  
jitterName Multiframe SHARKSv20210222 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe SHARKSv20210421 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe ULTRAVISTADR4 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSDR1 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSDR2 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSDR3 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSDR4 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSDR5 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSDR6 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20120926 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20130417 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20140409 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20150108 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20160114 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20160507 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20170630 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20180419 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VHSv20201209 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIDEODR2 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIDEODR3 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIDEODR4 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIDEODR5 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIDEOv20100513 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIDEOv20111208 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGDR2 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGDR3 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGDR4 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20110714 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20111019 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20130417 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20140402 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20150421 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20151230 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20160406 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20161202 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VIKINGv20170715 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCDEEPv20230713 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCDR1 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCDR2 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCDR3 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCDR4 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCDR5 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20110816 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20110909 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20120126 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20121128 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20130304 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20130805 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20140428 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20140903 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20150309 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20151218 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20160311 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20160822 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20170109 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20170411 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20171101 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20180702 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20181120 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20191212 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20210708 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20230816 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VMCv20240226 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VVVDR1 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VVVDR2 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VVVDR5 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VVVXDR1 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VVVv20100531 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName Multiframe VVVv20110718 Name of jitter pattern {image primary HDU keyword: JITTR_ID} varchar 8   NONE  
jitterName sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Name of jitter pattern varchar 8   NONE  
jitterX Multiframe SHARKSv20210222 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe SHARKSv20210421 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe ULTRAVISTADR4 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSDR1 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSDR2 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSDR3 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSDR4 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSDR5 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSDR6 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20120926 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20130417 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20140409 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20150108 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20160114 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20160507 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20170630 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20180419 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VHSv20201209 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIDEODR2 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIDEODR3 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIDEODR4 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIDEODR5 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIDEOv20100513 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIDEOv20111208 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGDR2 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGDR3 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGDR4 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20110714 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20111019 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20130417 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20140402 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20150421 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20151230 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20160406 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20161202 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VIKINGv20170715 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCDEEPv20230713 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCDR1 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCDR2 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCDR3 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCDR4 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCDR5 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20110816 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20110909 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20120126 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20121128 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20130304 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20130805 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20140428 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20140903 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20150309 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20151218 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20160311 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20160822 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20170109 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20170411 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20171101 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20180702 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20181120 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20191212 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20210708 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20230816 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VMCv20240226 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VVVDR1 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VVVDR2 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VVVDR5 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VVVXDR1 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VVVv20100531 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX Multiframe VVVv20110718 X offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_X} real 4   -0.9999995e9  
jitterX sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC X offset in jitter pattern [arcsec] real 4   -0.9999995e9  
jitterY Multiframe SHARKSv20210222 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe SHARKSv20210421 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe ULTRAVISTADR4 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSDR1 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSDR2 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSDR3 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSDR4 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSDR5 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSDR6 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20120926 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20130417 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20140409 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20150108 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20160114 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20160507 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20170630 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20180419 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VHSv20201209 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIDEODR2 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIDEODR3 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIDEODR4 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIDEODR5 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIDEOv20100513 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIDEOv20111208 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGDR2 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGDR3 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGDR4 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20110714 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20111019 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20130417 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20140402 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20150421 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20151230 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20160406 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20161202 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VIKINGv20170715 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCDEEPv20230713 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCDR1 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCDR2 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCDR3 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCDR4 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCDR5 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20110816 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20110909 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20120126 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20121128 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20130304 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20130805 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20140428 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20140903 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20150309 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20151218 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20160311 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20160822 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20170109 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20170411 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20171101 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20180702 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20181120 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20191212 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20210708 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20230816 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VMCv20240226 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VVVDR1 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VVVDR2 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VVVDR5 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VVVXDR1 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VVVv20100531 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY Multiframe VVVv20110718 Y offset in jitter pattern [arcsec] {image primary HDU keyword: JITTER_Y} real 4   -0.9999995e9  
jitterY sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Y offset in jitter pattern [arcsec] real 4   -0.9999995e9  
jKronJky ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J calibrated flux (Kron) real 4 jansky -0.9999995e9 phot.flux
jKronJkyErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error in extended source J calibrated flux (Kron) real 4 janksy -0.9999995e9 stat.error
jKronLup ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J luptitude (Kron) real 4 lup -0.9999995e9 phot.lup
jKronLupErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error in extended source J luptitude (Kron) real 4 lup -0.9999995e9 stat.error
jKronMag ultravistaSource ULTRAVISTADR4 Extended source J mag (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jKronMag ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J magnitude (Kron) real 4 mag -0.9999995e9 phot.mag
jKronMag videoSource VIDEODR4 Extended source J mag (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jKronMag videoSource VIDEODR5 Extended source J mag (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jKronMagErr ultravistaSource ULTRAVISTADR4 Extended source J mag error (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jKronMagErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error in extended source J magnitude (Kron) real 4 mag -0.9999995e9 stat.error
jKronMagErr videoSource VIDEODR4 Extended source J mag error (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jKronMagErr videoSource VIDEODR5 Extended source J mag error (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jksiWS vmcVariability VMCDR1 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9  
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCDR2 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCDR3 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCDR4 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCDR5 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20110816 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9  
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20110909 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9  
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20120126 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9  
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20121128 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20130304 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20130805 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20140428 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20140903 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20150309 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20151218 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20160311 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20160822 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20170109 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20170411 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20171101 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20180702 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20181120 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20191212 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20210708 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20230816 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcVariability VMCv20240226 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
jksiWS vmcdeepVariability VMCDEEPv20230713 Welch-Stetson statistic between J and Ks. This assumes colour does not vary much and helps remove variation due to a few poor detections real 4   -0.9999995e9 stat.param;em.IR.J;em.IR.K
The Welch-Stetson statistic is a measure of the correlation of the variability between two bands. We use the calculation in Welch D.L. and Stetson P.B. 1993, AJ, 105, 5, which is also used in Sesar et al. 2007, AJ, 134, 2236. We use the aperMag3 magnitude when comparing between bands.
Jmag mcps_lmcSource, mcps_smcSource MCPS The J band magnitude (from 2MASS) (0.00 if star not detected.) real 4 mag    
Jmag vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR5 VVV DR4 J photometry {catalogue TType keyword: Jmag} real 4 mag -999999500.0  
jMag ukirtFSstars VIDEOv20100513 J band total magnitude on the MKO(UFTI) system real 4 mag   phot.mag
jMag ukirtFSstars VIKINGv20110714 J band total magnitude on the MKO(UFTI) system real 4 mag   phot.mag
jMag ukirtFSstars VVVv20100531 J band total magnitude on the MKO(UFTI) system real 4 mag   phot.mag
jMag vhsSourceRemeasurement VHSDR1 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
jMag videoSourceRemeasurement VIDEOv20100513 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
jMag vikingSourceRemeasurement VIKINGv20110714 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
jMag vikingSourceRemeasurement VIKINGv20111019 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
jMag vmcSourceRemeasurement VMCv20110816 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
jMag vmcSourceRemeasurement VMCv20110909 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
jMag vvvSourceRemeasurement VVVv20100531 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
jMag vvvSourceRemeasurement VVVv20110718 J mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
Jmag2MASS spitzer_smcSource SPITZER The 2MASS J band magnitude. real 4 mag    
Jmag_2MASS ravedr5Source RAVE J selected default magnitude from 2MASS real 4 mag magnitude phot.mag;em.IR.J
Jmag_DENIS ravedr5Source RAVE J selected default magnitude from DENIS real 4 mag magnitude phot.mag;em.IR.J
jMagErr ukirtFSstars VIDEOv20100513 J band magnitude error real 4 mag   stat.error
jMagErr ukirtFSstars VIKINGv20110714 J band magnitude error real 4 mag   stat.error
jMagErr ukirtFSstars VVVv20100531 J band magnitude error real 4 mag   stat.error
jMagErr vhsSourceRemeasurement VHSDR1 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagErr videoSourceRemeasurement VIDEOv20100513 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagErr vikingSourceRemeasurement VIKINGv20110714 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagErr vikingSourceRemeasurement VIKINGv20111019 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagErr vmcCepheidVariables VMCDR4 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20160311 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20160822 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20170109 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20170411 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20171101 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20180702 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20181120 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20191212 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20210708 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20230816 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcCepheidVariables VMCv20240226 Error in intensity-averaged J band magnitude {catalogue TType keyword: e_Jmag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jMagErr vmcSourceRemeasurement VMCv20110816 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagErr vmcSourceRemeasurement VMCv20110909 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagErr vvvSourceRemeasurement VVVv20100531 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagErr vvvSourceRemeasurement VVVv20110718 Error in J mag real 4 mag -0.9999995e9 stat.error
jMagMAD ultravistaMapLcVariability ULTRAVISTADR4 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD ultravistaVariability ULTRAVISTADR4 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD videoVariability VIDEODR2 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD videoVariability VIDEODR3 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD videoVariability VIDEODR4 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD videoVariability VIDEODR5 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD videoVariability VIDEOv20100513 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD videoVariability VIDEOv20111208 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGDR2 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGDR3 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGDR4 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20110714 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20111019 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20130417 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20140402 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20150421 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20151230 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20160406 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20161202 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vikingVariability VIKINGv20170715 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCDR1 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCDR2 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCDR3 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCDR4 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCDR5 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20110816 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20110909 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20120126 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20121128 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20130304 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20130805 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20140428 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20140903 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20150309 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20151218 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20160311 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20160822 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20170109 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20170411 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20171101 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20180702 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20181120 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20191212 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20210708 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20230816 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcVariability VMCv20240226 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vmcdeepVariability VMCDEEPv20230713 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vvvVariability VVVDR5 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9 stat.err;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagMAD vvvVariability VVVv20100531 Median Absolute Deviation of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms ultravistaMapLcVariability ULTRAVISTADR4 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms ultravistaVariability ULTRAVISTADR4 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms videoVariability VIDEODR2 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms videoVariability VIDEODR3 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms videoVariability VIDEODR4 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms videoVariability VIDEODR5 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms videoVariability VIDEOv20100513 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms videoVariability VIDEOv20111208 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGDR2 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGDR3 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGDR4 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20110714 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20111019 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20130417 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20140402 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20150421 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20151230 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20160406 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20161202 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vikingVariability VIKINGv20170715 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCDR1 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCDR2 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCDR3 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCDR4 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCDR5 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20110816 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20110909 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20120126 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20121128 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20130304 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20130805 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20140428 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20140903 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20150309 rms of J magnitude real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20151218 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20160311 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20160822 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20170109 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20170411 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20171101 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20180702 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20181120 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20191212 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20210708 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20230816 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcVariability VMCv20240226 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vmcdeepVariability VMCDEEPv20230713 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vvvVariability VVVDR5 rms of J magnitude real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMagRms vvvVariability VVVv20100531 rms of J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmaxCadence ultravistaMapLcVariability ULTRAVISTADR4 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence ultravistaVariability ULTRAVISTADR4 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence videoVariability VIDEODR2 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence videoVariability VIDEODR3 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence videoVariability VIDEODR4 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence videoVariability VIDEODR5 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence videoVariability VIDEOv20100513 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence videoVariability VIDEOv20111208 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGDR2 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGDR3 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGDR4 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20110714 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20111019 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20130417 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20140402 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20150421 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20151230 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20160406 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20161202 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vikingVariability VIKINGv20170715 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCDR1 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCDR2 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCDR3 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCDR4 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCDR5 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20110816 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20110909 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20120126 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20121128 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20130304 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20130805 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20140428 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20140903 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20150309 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20151218 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20160311 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20160822 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20170109 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20170411 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20171101 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20180702 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20181120 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20191212 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20210708 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20230816 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcVariability VMCv20240226 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vmcdeepVariability VMCDEEPv20230713 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vvvVariability VVVDR5 maximum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.max
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmaxCadence vvvVariability VVVv20100531 maximum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jMaxMag ultravistaMapLcVariability ULTRAVISTADR4 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag ultravistaVariability ULTRAVISTADR4 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag videoVariability VIDEODR2 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag videoVariability VIDEODR3 Maximum magnitude in J band, of good detections real 4   -0.9999995e9 phot.mag;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag videoVariability VIDEODR4 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag videoVariability VIDEODR5 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag videoVariability VIDEOv20100513 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag videoVariability VIDEOv20111208 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGDR2 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGDR3 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGDR4 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20110714 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20111019 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20130417 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20140402 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20150421 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20151230 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20160406 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20161202 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vikingVariability VIKINGv20170715 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCDR1 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCDR2 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCDR3 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCDR4 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCDR5 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20110816 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20110909 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20120126 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20121128 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20130304 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20130805 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20140428 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20140903 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20150309 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20151218 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20160311 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20160822 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20170109 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20170411 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20171101 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20180702 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20181120 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20191212 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20210708 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20230816 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcVariability VMCv20240226 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vmcdeepVariability VMCDEEPv20230713 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vvvVariability VVVDR5 Maximum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.max
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMaxMag vvvVariability VVVv20100531 Maximum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMeanMag vmcCepheidVariables VMCDR4 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20160311 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20160822 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20170109 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20170411 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20171101 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20180702 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20181120 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20191212 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20210708 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20230816 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jMeanMag vmcCepheidVariables VMCv20240226 Intensity-averaged J band magnitude {catalogue TType keyword: Jmag} real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.J
jmeanMag ultravistaMapLcVariability ULTRAVISTADR4 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag ultravistaVariability ULTRAVISTADR4 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag videoVariability VIDEODR2 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag videoVariability VIDEODR3 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag videoVariability VIDEODR4 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag videoVariability VIDEODR5 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag videoVariability VIDEOv20100513 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag videoVariability VIDEOv20111208 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGDR2 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGDR3 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGDR4 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20110714 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20111019 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20130417 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20140402 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20150421 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20151230 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20160406 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20161202 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vikingVariability VIKINGv20170715 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCDR1 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCDR2 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCDR3 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCDR4 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCDR5 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20110816 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20110909 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20120126 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20121128 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20130304 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20130805 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20140428 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20140903 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20150309 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20151218 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20160311 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20160822 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20170109 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20170411 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20171101 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20180702 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20181120 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20191212 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20210708 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20230816 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcVariability VMCv20240226 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vmcdeepVariability VMCDEEPv20230713 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vvvVariability VVVDR5 Mean J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.mean;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmeanMag vvvVariability VVVv20100531 Mean J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
Jmeas vvvProperMotionCatalogue VVVDR5 Is there a J band measurment for this frame tinyint 1      
jmedCadence ultravistaMapLcVariability ULTRAVISTADR4 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence ultravistaVariability ULTRAVISTADR4 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence videoVariability VIDEODR2 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence videoVariability VIDEODR3 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence videoVariability VIDEODR4 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence videoVariability VIDEODR5 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence videoVariability VIDEOv20100513 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence videoVariability VIDEOv20111208 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGDR2 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGDR3 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGDR4 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20110714 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20111019 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20130417 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20140402 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20150421 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20151230 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20160406 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20161202 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vikingVariability VIKINGv20170715 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCDR1 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCDR2 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCDR3 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCDR4 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCDR5 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20110816 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20110909 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20120126 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20121128 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20130304 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20130805 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20140428 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20140903 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20150309 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20151218 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20160311 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20160822 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20170109 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20170411 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20171101 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20180702 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20181120 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20191212 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20210708 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20230816 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcVariability VMCv20240226 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vmcdeepVariability VMCDEEPv20230713 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vvvVariability VVVDR5 median gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.median
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedCadence vvvVariability VVVv20100531 median gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jmedianMag ultravistaMapLcVariability ULTRAVISTADR4 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag ultravistaVariability ULTRAVISTADR4 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag videoVariability VIDEODR2 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag videoVariability VIDEODR3 Median J magnitude real 4 mag -0.9999995e9 phot.mag;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag videoVariability VIDEODR4 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag videoVariability VIDEODR5 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag videoVariability VIDEOv20100513 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag videoVariability VIDEOv20111208 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGDR2 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGDR3 Median J magnitude real 4 mag -0.9999995e9 phot.mag;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGDR4 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20110714 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20111019 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20130417 Median J magnitude real 4 mag -0.9999995e9 phot.mag;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20140402 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20150421 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20151230 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20160406 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20161202 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vikingVariability VIKINGv20170715 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCDR1 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCDR2 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCDR3 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCDR4 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCDR5 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20110816 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20110909 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20120126 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20121128 Median J magnitude real 4 mag -0.9999995e9 phot.mag;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20130304 Median J magnitude real 4 mag -0.9999995e9 phot.mag;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20130805 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20140428 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20140903 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20150309 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20151218 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20160311 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20160822 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20170109 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20170411 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20171101 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20180702 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20181120 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20191212 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20210708 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20230816 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcVariability VMCv20240226 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vmcdeepVariability VMCDEEPv20230713 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vvvVariability VVVDR5 Median J magnitude real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.median;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmedianMag vvvVariability VVVv20100531 Median J magnitude real 4 mag -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jmfID ultravistaMergeLog, ultravistaRemeasMergeLog ULTRAVISTADR4 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSDR1 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vhsMergeLog VHSDR2 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vhsMergeLog VHSDR3 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSDR4 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSDR5 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSDR6 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSv20120926 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vhsMergeLog VHSv20130417 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vhsMergeLog VHSv20140409 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSv20150108 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSv20160114 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSv20160507 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSv20170630 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSv20180419 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vhsMergeLog VHSv20201209 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID videoMergeLog VIDEODR2 the UID of the relevant J multiframe bigint 8     obs.field
jmfID videoMergeLog VIDEODR3 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID videoMergeLog VIDEODR4 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID videoMergeLog VIDEODR5 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID videoMergeLog VIDEOv20100513 the UID of the relevant J multiframe bigint 8     obs.field
jmfID videoMergeLog VIDEOv20111208 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vikingMergeLog VIKINGDR2 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vikingMergeLog VIKINGDR3 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vikingMergeLog VIKINGDR4 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vikingMergeLog VIKINGv20110714 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vikingMergeLog VIKINGv20111019 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vikingMergeLog VIKINGv20130417 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vikingMergeLog VIKINGv20140402 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vikingMergeLog VIKINGv20150421 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vikingMergeLog VIKINGv20151230 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vikingMergeLog VIKINGv20160406 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vikingMergeLog VIKINGv20161202 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vikingMergeLog VIKINGv20170715 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20160909 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20170124 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vmcMergeLog VMCDR2 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vmcMergeLog VMCDR3 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCDR4 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCDR5 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20110816 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vmcMergeLog VMCv20110909 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vmcMergeLog VMCv20120126 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vmcMergeLog VMCv20121128 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vmcMergeLog VMCv20130304 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vmcMergeLog VMCv20130805 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vmcMergeLog VMCv20140428 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20140903 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20150309 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20151218 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20160311 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20160822 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20170109 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20170411 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20171101 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20180702 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20181120 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20191212 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20210708 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20230816 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog VMCv20240226 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vmcMergeLog, vmcSynopticMergeLog VMCDR1 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vmcdeepMergeLog, vmcdeepSynopticMergeLog VMCDEEPv20230713 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vvvMergeLog VVVDR2 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmfID vvvMergeLog VVVv20100531 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vvvMergeLog VVVv20110718 the UID of the relevant J multiframe bigint 8     obs.field
jmfID vvvMergeLog, vvvPsfDaophotJKsMergeLog VVVDR5 the UID of the relevant J multiframe bigint 8     meta.id;obs.field;em.IR.J
jmfID vvvMergeLog, vvvSynopticMergeLog VVVDR1 the UID of the relevant J multiframe bigint 8     meta.id;obs.field
jmh vhsSourceRemeasurement VHSDR1 Default colour J-H (using appropriate mags) real 4 mag   PHOT_COLOR
jmh videoSourceRemeasurement VIDEOv20100513 Default colour J-H (using appropriate mags) real 4 mag   PHOT_COLOR
jmh vikingSourceRemeasurement VIKINGv20110714 Default colour J-H (using appropriate mags) real 4 mag   PHOT_COLOR
jmh vikingSourceRemeasurement VIKINGv20111019 Default colour J-H (using appropriate mags) real 4 mag   PHOT_COLOR
jmh vvvSourceRemeasurement VVVv20100531 Default colour J-H (using appropriate mags) real 4 mag   PHOT_COLOR
jmh vvvSourceRemeasurement VVVv20110718 Default colour J-H (using appropriate mags) real 4 mag   PHOT_COLOR
jmhErr vhsSourceRemeasurement VHSDR1 Error on colour J-H real 4 mag   stat.error
jmhErr videoSourceRemeasurement VIDEOv20100513 Error on colour J-H real 4 mag   stat.error
jmhErr vikingSourceRemeasurement VIKINGv20110714 Error on colour J-H real 4 mag   stat.error
jmhErr vikingSourceRemeasurement VIKINGv20111019 Error on colour J-H real 4 mag   stat.error
jmhErr vvvSourceRemeasurement VVVv20100531 Error on colour J-H real 4 mag   stat.error
jmhErr vvvSourceRemeasurement VVVv20110718 Error on colour J-H real 4 mag   stat.error
jmhExt ultravistaSource ULTRAVISTADR4 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSDR1 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSDR2 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSDR3 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSDR4 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSDR5 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSDR6 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20120926 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20130417 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20140409 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20150108 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20160114 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20160507 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20170630 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20180419 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vhsSource VHSv20201209 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt videoSource VIDEODR2 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt videoSource VIDEODR3 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt videoSource VIDEODR4 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt videoSource VIDEODR5 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt videoSource VIDEOv20100513 Extended source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt videoSource VIDEOv20111208 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGDR2 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGDR3 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGDR4 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20110714 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20111019 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20130417 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20140402 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20150421 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20151230 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20160406 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20161202 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingSource VIKINGv20170715 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source colour J-H (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExt vvvSource VVVv20100531 Extended source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr ultravistaSource ULTRAVISTADR4 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSDR1 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSDR2 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSDR3 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSDR4 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSDR5 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSDR6 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20120926 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20130417 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20140409 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20150108 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20160114 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20160507 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20170630 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20180419 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vhsSource VHSv20201209 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr videoSource VIDEODR2 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr videoSource VIDEODR3 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr videoSource VIDEODR4 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr videoSource VIDEODR5 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr videoSource VIDEOv20100513 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr videoSource VIDEOv20111208 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGDR2 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGDR3 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGDR4 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20110714 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20111019 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20130417 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20140402 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20150421 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20151230 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20160406 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20161202 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingSource VIKINGv20170715 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtErr vvvSource VVVv20100531 Error on extended source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJky ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source colour calibrated flux H/J (using aperJkyNoAperCorr3) real 4 jansky -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJky vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source colour calibrated flux H/J (using aperJkyNoAperCorr3) real 4 jansky -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJky vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source colour calibrated flux H/J (using aperJkyNoAperCorr3) real 4 jansky -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJkyErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error on extended source colour calibrated flux H/J real 4 jansky -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJkyErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error on extended source colour calibrated flux H/J real 4 jansky -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtJkyErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error on extended source colour calibrated flux H/J real 4 jansky -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLup ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source colour luptitudeJ-H (using aperLupNoAperCorr3) real 4 lup -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLup vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source colour luptitudeJ-H (using aperLupNoAperCorr3) real 4 lup -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLup vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source colour luptitudeJ-H (using aperLupNoAperCorr3) real 4 lup -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLupErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error on extended source colour luptitude J-H real 4 lup -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLupErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error on extended source colour luptitude J-H real 4 lup -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhExtLupErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error on extended source colour luptitude J-H real 4 lup -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt ultravistaSource ULTRAVISTADR4 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt ultravistaSourceRemeasurement ULTRAVISTADR4 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSDR1 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSDR2 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSDR3 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSDR4 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSDR5 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSDR6 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20120926 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20130417 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20140409 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20150108 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20160114 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20160507 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20170630 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20180419 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vhsSource VHSv20201209 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt videoSource VIDEODR2 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt videoSource VIDEODR3 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt videoSource VIDEODR4 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt videoSource VIDEODR5 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt videoSource VIDEOv20100513 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt videoSource VIDEOv20111208 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGDR2 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGDR3 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGDR4 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20110714 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20111019 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20130417 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20140402 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20150421 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20151230 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20160406 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20161202 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingSource VIKINGv20170715 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vvvPsfDophotZYJHKsSource VVVDR5 Point source colour J-H (using PsfMag) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vvvSource VVVDR2 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vvvSource VVVDR5 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vvvSource VVVv20100531 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vvvSource VVVv20110718 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPnt vvvSource, vvvSynopticSource VVVDR1 Point source colour J-H (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr ultravistaSource ULTRAVISTADR4 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSDR1 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSDR2 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSDR3 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSDR4 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSDR5 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSDR6 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20120926 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20130417 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20140409 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20150108 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20160114 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20160507 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20170630 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20180419 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vhsSource VHSv20201209 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr videoSource VIDEODR2 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr videoSource VIDEODR3 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr videoSource VIDEODR4 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr videoSource VIDEODR5 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr videoSource VIDEOv20100513 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr videoSource VIDEOv20111208 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGDR2 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGDR3 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGDR4 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20110714 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20111019 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20130417 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20140402 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20150421 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20151230 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20160406 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20161202 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingSource VIKINGv20170715 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vvvPsfDophotZYJHKsSource VVVDR5 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vvvSource VVVDR2 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vvvSource VVVDR5 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.H
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vvvSource VVVv20100531 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vvvSource VVVv20110718 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntErr vvvSource, vvvSynopticSource VVVDR1 Error on point source colour J-H real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJky ultravistaSourceRemeasurement ULTRAVISTADR4 Point source colour calibrated flux H/J (using aperJky3) real 4 jansky -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJky vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source colour calibrated flux H/J (using aperJky3) real 4 jansky -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJky vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source colour calibrated flux H/J (using aperJky3) real 4 jansky -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJkyErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error on point source colour calibrated flux H/J real 4 jansky -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJkyErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error on point source colour calibrated flux H/J real 4 jansky -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntJkyErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error on point source colour calibrated flux H/J real 4 jansky -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLup ultravistaSourceRemeasurement ULTRAVISTADR4 Point source colour luptitude J-H (using aperLup3) real 4 lup -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLup vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source colour luptitude J-H (using aperLup3) real 4 lup -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLup vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source colour luptitude J-H (using aperLup3) real 4 lup -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLupErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error on point source colour luptitude J-H real 4 lup -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLupErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error on point source colour luptitude J-H real 4 lup -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmhPntLupErr vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error on point source colour luptitude J-H real 4 lup -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jminCadence ultravistaMapLcVariability ULTRAVISTADR4 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence ultravistaVariability ULTRAVISTADR4 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence videoVariability VIDEODR2 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence videoVariability VIDEODR3 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence videoVariability VIDEODR4 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence videoVariability VIDEODR5 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence videoVariability VIDEOv20100513 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence videoVariability VIDEOv20111208 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGDR2 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGDR3 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGDR4 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20110714 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20111019 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20130417 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20140402 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20150421 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20151230 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20160406 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20161202 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vikingVariability VIKINGv20170715 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCDR1 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCDR2 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCDR3 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCDR4 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCDR5 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20110816 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20110909 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20120126 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20121128 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20130304 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20130805 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20140428 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20140903 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20150309 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20151218 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20160311 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20160822 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20170109 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20170411 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20171101 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20180702 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20181120 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20191212 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20210708 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20230816 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcVariability VMCv20240226 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vmcdeepVariability VMCDEEPv20230713 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vvvVariability VVVDR5 minimum gap between observations real 4 days -0.9999995e9 time.interval;obs;stat.min
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jminCadence vvvVariability VVVv20100531 minimum gap between observations real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jMinMag ultravistaMapLcVariability ULTRAVISTADR4 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag ultravistaVariability ULTRAVISTADR4 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag videoVariability VIDEODR2 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag videoVariability VIDEODR3 Minimum magnitude in J band, of good detections real 4   -0.9999995e9 phot.mag;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag videoVariability VIDEODR4 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag videoVariability VIDEODR5 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag videoVariability VIDEOv20100513 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag videoVariability VIDEOv20111208 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGDR2 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGDR3 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGDR4 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20110714 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20111019 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20130417 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20140402 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20150421 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20151230 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20160406 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20161202 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vikingVariability VIKINGv20170715 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCDR1 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCDR2 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCDR3 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCDR4 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCDR5 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20110816 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20110909 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20120126 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20121128 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20130304 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20130805 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20140428 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20140903 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20150309 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20151218 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20160311 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20160822 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20170109 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20170411 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20171101 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20180702 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20181120 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20191212 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20210708 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20230816 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcVariability VMCv20240226 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vmcdeepVariability VMCDEEPv20230713 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vvvVariability VVVDR5 Minimum magnitude in J band, of good detections real 4 mag -0.9999995e9 phot.mag;em.IR.J;stat.min
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMinMag vvvVariability VVVv20100531 Minimum magnitude in J band, of good detections real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jMjd ultravistaSource ULTRAVISTADR4 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd ultravistaSourceRemeasurement ULTRAVISTADR4 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vhsSource VHSDR5 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vhsSource VHSDR6 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vhsSource VHSv20160114 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vhsSource VHSv20160507 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vhsSource VHSv20170630 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vhsSource VHSv20180419 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vhsSource VHSv20201209 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vikingSource VIKINGv20151230 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vikingSource VIKINGv20160406 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vikingSource VIKINGv20161202 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vikingSource VIKINGv20170715 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vmcSource VMCDR5 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource VMCv20151218 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vmcSource VMCv20160311 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vmcSource VMCv20160822 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vmcSource VMCv20170109 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vmcSource VMCv20170411 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vmcSource VMCv20171101 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource VMCv20180702 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource VMCv20181120 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource VMCv20191212 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource VMCv20210708 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource VMCv20230816 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource VMCv20240226 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcSource, vmcSynopticSource VMCDR4 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vmcdeepSource VMCDEEPv20230713 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vmcdeepSynopticSource VMCDEEPv20230713 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;meta.main
jMjd vvvSource VVVDR5 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch;em.IR.J
jMjd vvvSynopticSource VVVDR1 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jMjd vvvSynopticSource VVVDR2 Modified Julian Day in J band float 8 days -0.9999995e9 time.epoch
jmks vmcSourceRemeasurement VMCv20110816 Default colour J-Ks (using appropriate mags) real 4 mag   PHOT_COLOR
jmks vmcSourceRemeasurement VMCv20110909 Default colour J-Ks (using appropriate mags) real 4 mag   PHOT_COLOR
jmksErr vmcSourceRemeasurement VMCv20110816 Error on colour J-Ks real 4 mag   stat.error
jmksErr vmcSourceRemeasurement VMCv20110909 Error on colour J-Ks real 4 mag   stat.error
jmksExt vhsSource VHSDR1 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSDR2 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSDR3 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSDR4 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSDR5 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSDR6 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20120926 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20130417 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20140409 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20150108 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20160114 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20160507 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20170630 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20180419 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vhsSource VHSv20201209 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCDR1 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCDR2 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCDR3 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCDR4 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCDR5 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20110816 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20110909 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20120126 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20121128 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20130304 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20130805 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20140428 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20140903 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20150309 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20151218 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20160311 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20160822 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20170109 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20170411 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20171101 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20180702 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20181120 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20191212 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20210708 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20230816 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcSource VMCv20240226 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExt vmcdeepSource VMCDEEPv20230713 Extended source colour J-Ks (using aperMagNoAperCorr3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSDR1 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSDR2 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSDR3 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSDR4 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSDR5 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSDR6 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20120926 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20130417 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20140409 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20150108 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20160114 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20160507 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20170630 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20180419 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vhsSource VHSv20201209 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCDR1 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCDR2 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCDR3 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCDR4 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCDR5 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20110816 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20110909 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20120126 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20121128 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20130304 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20130805 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20140428 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20140903 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20150309 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20151218 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20160311 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20160822 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20170109 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20170411 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20171101 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20180702 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20181120 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20191212 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20210708 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20230816 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcSource VMCv20240226 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksExtErr vmcdeepSource VMCDEEPv20230713 Error on extended source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSDR1 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSDR2 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSDR3 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSDR4 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSDR5 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSDR6 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20120926 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20130417 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20140409 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20150108 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20160114 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20160507 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20170630 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20180419 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vhsSource VHSv20201209 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCDR2 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCDR3 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCDR4 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCDR5 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20110816 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20110909 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20120126 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20121128 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20130304 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20130805 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20140428 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20140903 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20150309 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20151218 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20160311 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20160822 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20170109 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20170411 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20171101 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20180702 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20181120 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20191212 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20210708 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20230816 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource VMCv20240226 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcSource, vmcSynopticSource VMCDR1 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 PHOT_COLOR
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Point source colour J-Ks (using aperMag3) real 4 mag -0.9999995e9 phot.color;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPnt vvvPsfDaophotJKsSource VVVDR5 Point source colour J-Ks (using PsfMag) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntCor vvvPsfDaophotJKsSource VVVDR5 Point source colour J-Ks (using PsfMagCor) real 4 mag -0.9999995e9 phot.color
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSDR1 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSDR2 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSDR3 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSDR4 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSDR5 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSDR6 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20120926 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20130417 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20140409 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20150108 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20160114 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20160507 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20170630 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20180419 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vhsSource VHSv20201209 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCDR2 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCDR3 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCDR4 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCDR5 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20110816 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20110909 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20120126 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20121128 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20130304 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20130805 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20140428 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20140903 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20150309 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20151218 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20160311 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20160822 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20170109 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20170411 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20171101 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20180702 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20181120 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20191212 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20210708 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20230816 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource VMCv20240226 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcSource, vmcSynopticSource VMCDR1 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPntErr vvvPsfDaophotJKsSource VVVDR5 Error on point source colour J-Ks real 4 mag -0.9999995e9 stat.error
Default colours from pairs of adjacent passbands within a given set (e.g. Y-J, J-H and H-K for YJHK) are recorded in the merged source table for ease of querying and speedy querying via indexing of these attributes. Presently, the point-source colours and extended source colours are computed from the aperture corrected AperMag3 fixed 2 arcsec aperture diameter measures (for consistent measurement across all passbands) and generally good signal-to-noise. At some point in the future, this may be changed such that point-source colours will be computed from the PSF-fitted measures and extended source colours computed from the 2-d Sersic model profile fits.
jmksPsf vmcPsfCatalogue VMCDR4 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfCatalogue VMCv20150309 J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfCatalogue VMCv20151218 J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfCatalogue VMCv20160311 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfCatalogue VMCv20160822 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfCatalogue VMCv20170109 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfCatalogue VMCv20170411 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfCatalogue VMCv20171101 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfSource VMCDR5 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfSource VMCv20180702 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfSource VMCv20181120 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfSource VMCv20191212 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfSource VMCv20210708 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfSource VMCv20230816 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsf vmcPsfSource VMCv20240226 J-Ks 3 pixels PSF fitting colour {catalogue TType keyword: JKs} real 4 mag -0.9999995e9 stat.fit.param;phot.color;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCDR4 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCv20150309 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCv20151218 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCv20160311 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCv20160822 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCv20170109 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCv20170411 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfCatalogue VMCv20171101 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfSource VMCDR5 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfSource VMCv20180702 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfSource VMCv20181120 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfSource VMCv20191212 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfSource VMCv20210708 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfSource VMCv20230816 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jmksPsfErr vmcPsfSource VMCv20240226 Error on J-Ks 3 pixels PSF fitting colour real 4 mag -0.9999995e9 stat.error;em.IR.J;em.IR.K
jndof ultravistaMapLcVariability ULTRAVISTADR4 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof ultravistaVariability ULTRAVISTADR4 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof videoVariability VIDEODR2 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof videoVariability VIDEODR3 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof videoVariability VIDEODR4 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof videoVariability VIDEODR5 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof videoVariability VIDEOv20100513 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof videoVariability VIDEOv20111208 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGDR2 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGDR3 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGDR4 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20110714 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20111019 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20130417 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20140402 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20150421 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20151230 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20160406 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20161202 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vikingVariability VIKINGv20170715 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCDR1 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCDR2 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCDR3 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCDR4 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCDR5 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20110816 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20110909 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20120126 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20121128 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20130304 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20130805 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20140428 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20140903 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20150309 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20151218 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20160311 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20160822 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20170109 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20170411 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20171101 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20180702 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20181120 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20191212 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20210708 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20230816 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcVariability VMCv20240226 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vmcdeepVariability VMCDEEPv20230713 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vvvVariability VVVDR5 Number of degrees of freedom for chisquare smallint 2   -9999 stat.fit.dof;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jndof vvvVariability VVVv20100531 Number of degrees of freedom for chisquare smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jnDofAst ultravistaVarFrameSetInfo ULTRAVISTADR4 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst videoVarFrameSetInfo VIDEODR2 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst videoVarFrameSetInfo VIDEODR3 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst videoVarFrameSetInfo VIDEODR4 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst videoVarFrameSetInfo VIDEODR5 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst videoVarFrameSetInfo VIDEOv20100513 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst videoVarFrameSetInfo VIDEOv20111208 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGDR2 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGDR3 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGDR4 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20110714 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20111019 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20130417 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20140402 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20150421 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20151230 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20160406 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20161202 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vikingVarFrameSetInfo VIKINGv20170715 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCDR1 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCDR2 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCDR3 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCDR4 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCDR5 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20110816 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20110909 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20120126 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20121128 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20130304 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20130805 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20140428 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20140903 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20150309 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20151218 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20160311 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20160822 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20170109 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20170411 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20171101 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20180702 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20181120 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20191212 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20210708 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20230816 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcVarFrameSetInfo VMCv20240226 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vmcdeepVarFrameSetInfo VMCDEEPv20230713 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vvvVarFrameSetInfo VVVDR5 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofAst vvvVarFrameSetInfo VVVv20100531 Number of degrees of freedom of astrometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
jnDofPht ultravistaMapLcVarFrameSetInfo, ultravistaVarFrameSetInfo ULTRAVISTADR4 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht videoVarFrameSetInfo VIDEODR2 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht videoVarFrameSetInfo VIDEODR3 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht videoVarFrameSetInfo VIDEODR4 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht videoVarFrameSetInfo VIDEODR5 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht videoVarFrameSetInfo VIDEOv20100513 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht videoVarFrameSetInfo VIDEOv20111208 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGDR2 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGDR3 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGDR4 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20110714 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20111019 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20130417 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20140402 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20150421 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20151230 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20160406 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20161202 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vikingVarFrameSetInfo VIKINGv20170715 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCDR1 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCDR2 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCDR3 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCDR4 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCDR5 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20110816 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20110909 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20120126 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20121128 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20130304 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20130805 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.NIR
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20140428 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20140903 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20150309 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20151218 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20160311 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20160822 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20170109 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20170411 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20171101 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20180702 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20181120 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20191212 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20210708 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20230816 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcVarFrameSetInfo VMCv20240226 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vmcdeepVarFrameSetInfo VMCDEEPv20230713 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vvvVarFrameSetInfo VVVDR5 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999 stat.fit.dof;stat.param;em.IR.J
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jnDofPht vvvVarFrameSetInfo VVVv20100531 Number of degrees of freedom of photometric fit in J band. smallint 2   -9999  
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
jNepochs vmcCepheidVariables VMCDR4 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20160311 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20160822 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20170109 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20170411 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20171101 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20180702 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20181120 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20191212 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20210708 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20230816 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jNepochs vmcCepheidVariables VMCv20240226 Number of J mag epochs {catalogue TType keyword: o_Jmag} int 4   -99999999 meta.number;em.IR.J
jnFlaggedObs ultravistaVariability ULTRAVISTADR4 Number of detections in J band flagged as potentially spurious by ultravistaDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs videoVariability VIDEODR2 Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs videoVariability VIDEODR3 Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs videoVariability VIDEODR4 Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs videoVariability VIDEODR5 Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs videoVariability VIDEOv20100513 Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs videoVariability VIDEOv20111208 Number of detections in J band flagged as potentially spurious by videoDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGDR2 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGDR3 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGDR4 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20110714 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20111019 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20130417 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20140402 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20150421 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20151230 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20160406 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20161202 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vikingVariability VIKINGv20170715 Number of detections in J band flagged as potentially spurious by vikingDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCDR1 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCDR2 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCDR3 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCDR4 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCDR5 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20110816 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20110909 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20120126 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20121128 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20130304 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20130805 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20140428 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20140903 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20150309 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20151218 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20160311 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20160822 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20170109 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20170411 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20171101 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20180702 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20181120 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20191212 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20210708 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20230816 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcVariability VMCv20240226 Number of detections in J band flagged as potentially spurious by vmcDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vmcdeepVariability VMCDEEPv20230713 Number of detections in J band flagged as potentially spurious by vmcdeepDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vvvVariability VVVDR5 Number of detections in J band flagged as potentially spurious by vvvDetection.ppErrBits int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnFlaggedObs vvvVariability VVVv20100531 Number of detections in J band flagged as potentially spurious by vvvDetection.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs ultravistaVariability ULTRAVISTADR4 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs videoVariability VIDEODR2 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs videoVariability VIDEODR3 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs videoVariability VIDEODR4 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs videoVariability VIDEODR5 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs videoVariability VIDEOv20100513 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs videoVariability VIDEOv20111208 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGDR2 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGDR3 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGDR4 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20110714 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20111019 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20130417 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20140402 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20150421 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20151230 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20160406 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20161202 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vikingVariability VIKINGv20170715 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCDR1 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCDR2 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCDR3 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCDR4 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCDR5 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20110816 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20110909 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20120126 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20121128 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20130304 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20130805 Number of good detections in J band int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20140428 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20140903 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20150309 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20151218 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20160311 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20160822 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20170109 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20170411 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20171101 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20180702 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20181120 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20191212 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20210708 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20230816 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcVariability VMCv20240226 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vmcdeepVariability VMCDEEPv20230713 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vvvVariability VVVDR5 Number of good detections in J band int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnGoodObs vvvVariability VVVv20100531 Number of good detections in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jNgt3sig ultravistaMapLcVariability ULTRAVISTADR4 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig ultravistaVariability ULTRAVISTADR4 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig videoVariability VIDEODR2 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig videoVariability VIDEODR3 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig videoVariability VIDEODR4 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig videoVariability VIDEODR5 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig videoVariability VIDEOv20100513 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig videoVariability VIDEOv20111208 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGDR2 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGDR3 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGDR4 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20110714 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20111019 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20130417 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20140402 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20150421 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20151230 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20160406 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20161202 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vikingVariability VIKINGv20170715 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCDR1 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCDR2 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCDR3 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCDR4 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCDR5 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20110816 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20110909 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20120126 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20121128 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20130304 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20130805 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20140428 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20140903 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20150309 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20151218 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20160311 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20160822 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20170109 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20170411 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20171101 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20180702 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20181120 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20191212 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20210708 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20230816 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcVariability VMCv20240226 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vmcdeepVariability VMCDEEPv20230713 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vvvVariability VVVDR5 Number of good detections in J-band that are more than 3 sigma deviations (jAperMagN < (jMeanMag-3*jMagRms) smallint 2   -9999 meta.number;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jNgt3sig vvvVariability VVVv20100531 Number of good detections in J-band that are more than 3 sigma deviations smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jnMissingObs ultravistaVariability ULTRAVISTADR4 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs videoVariability VIDEODR2 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs videoVariability VIDEODR3 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs videoVariability VIDEODR4 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs videoVariability VIDEODR5 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs videoVariability VIDEOv20100513 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs videoVariability VIDEOv20111208 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGDR2 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGDR3 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGDR4 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20110714 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20111019 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20130417 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20140402 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20150421 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20151230 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20160406 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20161202 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vikingVariability VIKINGv20170715 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCDR1 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCDR2 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCDR3 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCDR4 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCDR5 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20110816 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20110909 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20120126 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20121128 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20130304 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20130805 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.NIR
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20140428 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20140903 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20150309 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20151218 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20160311 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20160822 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20170109 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20170411 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20171101 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20180702 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20181120 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20191212 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20210708 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20230816 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcVariability VMCv20240226 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vmcdeepVariability VMCDEEPv20230713 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vvvVariability VVVDR5 Number of J band frames that this object should have been detected on and was not int 4   0 meta.number;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnMissingObs vvvVariability VVVv20100531 Number of J band frames that this object should have been detected on and was not int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnNegFlagObs ultravistaMapLcVariability ULTRAVISTADR4 Number of flagged negative measurements in J band by ultravistaMapRemeasurement.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnNegObs ultravistaMapLcVariability ULTRAVISTADR4 Number of unflagged negative measurements J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnPosFlagObs ultravistaMapLcVariability ULTRAVISTADR4 Number of flagged positive measurements in J band by ultravistaMapRemeasurement.ppErrBits int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jnPosObs ultravistaMapLcVariability ULTRAVISTADR4 Number of unflagged positive measurements in J band int 4   0  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
joinCriterion RequiredNeighbours SHARKSv20210222 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours SHARKSv20210421 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours ULTRAVISTADR4 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSDR1 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSDR2 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSDR3 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSDR4 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSDR5 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSDR6 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20120926 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20130417 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20150108 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20160114 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20160507 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20170630 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20180419 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VHSv20201209 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIDEODR2 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIDEODR3 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIDEODR4 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIDEODR5 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIDEOv20100513 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIDEOv20111208 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGDR2 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGDR3 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGDR4 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20110714 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20111019 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20130417 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20150421 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20151230 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20160406 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20161202 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VIKINGv20170715 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCDEEPv20230713 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCDR1 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCDR3 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCDR4 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCDR5 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20110816 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20110909 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20120126 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20121128 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20130304 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20130805 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20140428 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20140903 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20150309 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20151218 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20160311 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20160822 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20170109 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20170411 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20171101 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20180702 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20181120 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20191212 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20210708 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20230816 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VMCv20240226 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VSAQC the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VVVDR1 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VVVDR2 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VVVDR5 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VVVXDR1 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VVVv20100531 the join criterion (search radius for matches) real 4 degrees   ??
joinCriterion RequiredNeighbours VVVv20110718 the join criterion (search radius for matches) real 4 degrees   ??
JON grs_ngpSource, grs_ranSource, grs_sgpSource TWODFGRS Eyeball classification, value > 0 only for galaxies brighter than about 18th mag: 0 = noise; 1 = S0; 2 = elliptical; 3 = spiral; 4 = irregular; 5 = undetermined galaxy; 6 = star; 7 = star + star merger; 8 = galaxy + star merger; 9 = galaxy + galaxy merger int 4      
jPA ultravistaSource ULTRAVISTADR4 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA ultravistaSourceRemeasurement ULTRAVISTADR4 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vhsSource VHSDR2 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vhsSource VHSDR3 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSDR4 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSDR5 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSDR6 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSv20120926 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vhsSource VHSv20130417 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vhsSource VHSv20140409 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSv20150108 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSv20160114 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSv20160507 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSv20170630 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSv20180419 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource VHSv20201209 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vhsSource, vhsSourceRemeasurement VHSDR1 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA videoSource VIDEODR2 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA videoSource VIDEODR3 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA videoSource VIDEODR4 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA videoSource VIDEODR5 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA videoSource VIDEOv20111208 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA videoSource, videoSourceRemeasurement VIDEOv20100513 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingSource VIKINGDR2 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingSource VIKINGDR3 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingSource VIKINGDR4 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vikingSource VIKINGv20111019 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingSource VIKINGv20130417 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingSource VIKINGv20140402 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingSource VIKINGv20150421 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vikingSource VIKINGv20151230 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vikingSource VIKINGv20160406 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vikingSource VIKINGv20161202 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vikingSource VIKINGv20170715 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vikingSource, vikingSourceRemeasurement VIKINGv20110714 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource VMCDR2 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource VMCDR3 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCDR4 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCDR5 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20110909 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource VMCv20120126 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource VMCv20121128 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource VMCv20130304 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource VMCv20130805 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource VMCv20140428 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20140903 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20150309 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20151218 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20160311 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20160822 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20170109 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20170411 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20171101 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20180702 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20181120 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20191212 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20210708 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20230816 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource VMCv20240226 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vmcSource, vmcSourceRemeasurement VMCv20110816 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcSource, vmcSynopticSource VMCDR1 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vvvSource VVVDR2 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vvvSource VVVDR5 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng;em.IR.J
jPA vvvSource VVVv20110718 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vvvSource, vvvSourceRemeasurement VVVv20100531 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPA vvvSource, vvvSynopticSource VVVDR1 ellipse fit celestial orientation in J real 4 Degrees -0.9999995e9 pos.posAng
jPetroJky ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J calibrated flux (Petrosian) real 4 jansky -0.9999995e9 phot.flux
jPetroJkyErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error in extended source J calibrated flux (Petrosian) real 4 jansky -0.9999995e9 stat.error
jPetroLup ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J luptitude (Petrosian) real 4 lup -0.9999995e9 phot.lup
jPetroLupErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error in extended source J luptitude (Petrosian) real 4 lup -0.9999995e9 stat.error
jPetroMag ultravistaSource ULTRAVISTADR4 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag ultravistaSourceRemeasurement ULTRAVISTADR4 Extended source J magnitude (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vhsSource VHSDR1 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vhsSource VHSDR2 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vhsSource VHSDR3 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSDR4 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSDR5 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSDR6 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSv20120926 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vhsSource VHSv20130417 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vhsSource VHSv20140409 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSv20150108 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSv20160114 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSv20160507 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSv20170630 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSv20180419 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vhsSource VHSv20201209 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag videoSource VIDEODR2 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag videoSource VIDEODR3 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag videoSource VIDEODR4 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag videoSource VIDEODR5 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag videoSource VIDEOv20100513 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag videoSource VIDEOv20111208 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vikingSource VIKINGDR2 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vikingSource VIKINGDR3 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vikingSource VIKINGDR4 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vikingSource VIKINGv20110714 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vikingSource VIKINGv20111019 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vikingSource VIKINGv20130417 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vikingSource VIKINGv20140402 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vikingSource VIKINGv20150421 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vikingSource VIKINGv20151230 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vikingSource VIKINGv20160406 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vikingSource VIKINGv20161202 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vikingSource VIKINGv20170715 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCDR1 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vmcSource VMCDR2 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCDR3 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCDR4 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCDR5 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20110816 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vmcSource VMCv20110909 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vmcSource VMCv20120126 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vmcSource VMCv20121128 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vmcSource VMCv20130304 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag
jPetroMag vmcSource VMCv20130805 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20140428 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20140903 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20150309 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20151218 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20160311 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20160822 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20170109 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20170411 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20171101 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20180702 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20181120 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20191212 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20210708 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20230816 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcSource VMCv20240226 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMag vmcdeepSource VMCDEEPv20230713 Extended source J mag (Petrosian) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPetroMagErr ultravistaSource ULTRAVISTADR4 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr ultravistaSourceRemeasurement ULTRAVISTADR4 Error in extended source J magnitude (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vhsSource VHSDR1 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vhsSource VHSDR2 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vhsSource VHSDR3 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J
jPetroMagErr vhsSource VHSDR4 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr vhsSource VHSDR5 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vhsSource VHSDR6 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vhsSource VHSv20120926 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vhsSource VHSv20130417 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vhsSource VHSv20140409 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J
jPetroMagErr vhsSource VHSv20150108 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr vhsSource VHSv20160114 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vhsSource VHSv20160507 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vhsSource VHSv20170630 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vhsSource VHSv20180419 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vhsSource VHSv20201209 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr videoSource VIDEODR2 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr videoSource VIDEODR3 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr videoSource VIDEODR4 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr videoSource VIDEODR5 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr videoSource VIDEOv20100513 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr videoSource VIDEOv20111208 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vikingSource VIKINGDR2 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vikingSource VIKINGDR3 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vikingSource VIKINGDR4 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J
jPetroMagErr vikingSource VIKINGv20110714 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vikingSource VIKINGv20111019 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vikingSource VIKINGv20130417 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vikingSource VIKINGv20140402 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vikingSource VIKINGv20150421 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr vikingSource VIKINGv20151230 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vikingSource VIKINGv20160406 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vikingSource VIKINGv20161202 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vikingSource VIKINGv20170715 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCDR1 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCDR2 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCDR3 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr vmcSource VMCDR4 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCDR5 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20110816 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCv20110909 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCv20120126 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCv20121128 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCv20130304 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCv20130805 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error
jPetroMagErr vmcSource VMCv20140428 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J
jPetroMagErr vmcSource VMCv20140903 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr vmcSource VMCv20150309 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPetroMagErr vmcSource VMCv20151218 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20160311 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20160822 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20170109 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20170411 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20171101 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20180702 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20181120 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20191212 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20210708 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20230816 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcSource VMCv20240226 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPetroMagErr vmcdeepSource VMCDEEPv20230713 Error in extended source J mag (Petrosian) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jppErrBits ultravistaSource ULTRAVISTADR4 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
jppErrBits ultravistaSourceRemeasurement ULTRAVISTADR4 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSDR1 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSDR2 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSDR3 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSDR4 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSDR5 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSDR6 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20120926 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20130417 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20140409 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20150108 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20160114 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20160507 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20170630 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20180419 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSource VHSv20201209 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vhsSourceRemeasurement VHSDR1 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits videoSource VIDEODR2 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits videoSource VIDEODR3 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits videoSource VIDEODR4 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
jppErrBits videoSource VIDEODR5 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
jppErrBits videoSource VIDEOv20111208 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits videoSource, videoSourceRemeasurement VIDEOv20100513 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vikingSource VIKINGDR2 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGDR3 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGDR4 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20110714 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20111019 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20130417 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20140402 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20150421 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20151230 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20160406 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20161202 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSource VIKINGv20170715 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingSourceRemeasurement VIKINGv20110714 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vikingSourceRemeasurement VIKINGv20111019 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCDR2 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCDR3 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCDR4 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCDR5 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20110816 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20110909 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20120126 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20121128 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20130304 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20130805 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20140428 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20140903 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20150309 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20151218 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20160311 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20160822 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20170109 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20170411 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20171101 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20180702 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20181120 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20191212 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20210708 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20230816 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource VMCv20240226 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSource, vmcSynopticSource VMCDR1 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vmcSourceRemeasurement VMCv20110816 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vmcSourceRemeasurement VMCv20110909 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vvvSource VVVDR1 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vvvSource VVVDR2 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vvvSource VVVDR5 additional WFAU post-processing error bits in J int 4   0 meta.code;em.IR.J
jppErrBits vvvSource VVVv20110718 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vvvSource, vvvSourceRemeasurement VVVv20100531 additional WFAU post-processing error bits in J int 4   0 meta.code
jppErrBits vvvSynopticSource VVVDR1 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jppErrBits vvvSynopticSource VVVDR2 additional WFAU post-processing error bits in J int 4   0 meta.code
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues
1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues
2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles
3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.
jprobVar ultravistaMapLcVariability ULTRAVISTADR4 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar ultravistaVariability ULTRAVISTADR4 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar videoVariability VIDEODR2 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar videoVariability VIDEODR3 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar videoVariability VIDEODR4 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar videoVariability VIDEODR5 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar videoVariability VIDEOv20100513 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar videoVariability VIDEOv20111208 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGDR2 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGDR3 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGDR4 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20110714 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20111019 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20130417 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20140402 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20150421 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20151230 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20160406 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20161202 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vikingVariability VIKINGv20170715 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCDR1 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCDR2 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCDR3 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCDR4 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCDR5 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20110816 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20110909 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20120126 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20121128 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20130304 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20130805 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20140428 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20140903 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20150309 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20151218 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20160311 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20160822 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20170109 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20170411 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20171101 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20180702 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20181120 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20191212 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20210708 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20230816 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcVariability VMCv20240226 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vmcdeepVariability VMCDEEPv20230713 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vvvVariability VVVDR5 Probability of variable from chi-square (and other data) real 4   -0.9999995e9 stat.probability;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jprobVar vvvVariability VVVv20100531 Probability of variable from chi-square (and other data) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jPsfMag vhsSource VHSDR1 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vhsSource VHSDR2 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vhsSource VHSDR3 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSDR4 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSDR5 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSDR6 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSv20120926 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vhsSource VHSv20130417 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vhsSource VHSv20140409 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSv20150108 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSv20160114 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSv20160507 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSv20170630 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSv20180419 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vhsSource VHSv20201209 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag videoSource VIDEOv20100513 Not available in SE output real 4 mag -0.9999995e9 phot.mag
jPsfMag vikingSource VIKINGDR2 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vikingSource VIKINGDR3 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vikingSource VIKINGDR4 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vikingSource VIKINGv20110714 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vikingSource VIKINGv20111019 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vikingSource VIKINGv20130417 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vikingSource VIKINGv20140402 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vikingSource VIKINGv20150421 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vikingSource VIKINGv20151230 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vikingSource VIKINGv20160406 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vikingSource VIKINGv20161202 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vikingSource VIKINGv20170715 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCDR3 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCDR4 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20121128 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param
jPsfMag vmcPsfCatalogue VMCv20140428 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20140903 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20150309 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20151218 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20160311 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20160822 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20170109 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20170411 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfCatalogue VMCv20171101 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfDetections VMCv20180702 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfDetections VMCv20181120 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J
jPsfMag vmcPsfSource VMCDR5 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag vmcPsfSource VMCv20180702 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag vmcPsfSource VMCv20181120 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag vmcPsfSource VMCv20191212 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag vmcPsfSource VMCv20210708 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag vmcPsfSource VMCv20230816 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag vmcPsfSource VMCv20240226 3 pixels PSF fitting magnitude J filter {catalogue TType keyword: J_MAG} real 4 mag -0.9999995e9 stat.fit.param;phot.mag;em.IR.J;meta.main
jPsfMag vmcSource VMCDR1 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vmcSource VMCDR2 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCDR3 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCDR4 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCDR5 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20110816 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vmcSource VMCv20110909 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vmcSource VMCv20120126 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vmcSource VMCv20121128 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vmcSource VMCv20130304 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag
jPsfMag vmcSource VMCv20130805 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20140428 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20140903 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20150309 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20151218 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20160311 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20160822 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20170109 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20170411 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20171101 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20180702 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20181120 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20191212 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20210708 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20230816 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcSource VMCv20240226 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vmcdeepSource VMCDEEPv20230713 Point source profile-fitted J mag real 4 mag -0.9999995e9 phot.mag;em.IR.J
jPsfMag vvvPsfDaophotJKsSource VVVDR5 PSF magnitude in J band {catalogue TType keyword: J} real 4 mag -0.9999995e9 instr.det.psf;phot.mag;em.IR.J;meta.main
jPsfMag vvvPsfDophotZYJHKsSource VVVDR5 Mean PSF magnitude in J band {catalogue TType keyword: mag_J} real 4 mag -0.9999995e9 instr.det.psf;phot.mag;em.IR.J;meta.main
jPsfMagCor vvvPsfDaophotJKsSource VVVDR5 Reddening corrected PSF magnitude in J band (J_0) {catalogue TType keyword: cJ} real 4 mag -0.9999995e9 phys.absorption;instr.det.psf;phot.mag;em.IR.J
jPsfMagErr vhsSource VHSDR1 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vhsSource VHSDR2 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vhsSource VHSDR3 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J
jPsfMagErr vhsSource VHSDR4 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPsfMagErr vhsSource VHSDR5 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vhsSource VHSDR6 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vhsSource VHSv20120926 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vhsSource VHSv20130417 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vhsSource VHSv20140409 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J
jPsfMagErr vhsSource VHSv20150108 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPsfMagErr vhsSource VHSv20160114 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vhsSource VHSv20160507 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vhsSource VHSv20170630 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vhsSource VHSv20180419 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vhsSource VHSv20201209 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr videoSource VIDEOv20100513 Not available in SE output real 4 mag -0.9999995e9 stat.error
jPsfMagErr vikingSource VIKINGDR2 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vikingSource VIKINGDR3 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vikingSource VIKINGDR4 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J
jPsfMagErr vikingSource VIKINGv20110714 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vikingSource VIKINGv20111019 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vikingSource VIKINGv20130417 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vikingSource VIKINGv20140402 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vikingSource VIKINGv20150421 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPsfMagErr vikingSource VIKINGv20151230 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vikingSource VIKINGv20160406 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vikingSource VIKINGv20161202 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vikingSource VIKINGv20170715 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCDR3 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCDR4 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20121128 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcPsfCatalogue VMCv20140428 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20140903 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20150309 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20151218 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20160311 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20160822 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20170109 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20170411 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfCatalogue VMCv20171101 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfDetections VMCv20181120 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcPsfDetections, vmcPsfSource VMCv20180702 PSF error J filter {catalogue TType keyword: J_ERR} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCDR1 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCDR2 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCDR3 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPsfMagErr vmcSource VMCDR4 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCDR5 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20110816 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCv20110909 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCv20120126 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCv20121128 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCv20130304 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCv20130805 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error
jPsfMagErr vmcSource VMCv20140428 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J
jPsfMagErr vmcSource VMCv20140903 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPsfMagErr vmcSource VMCv20150309 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jPsfMagErr vmcSource VMCv20151218 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20160311 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20160822 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20170109 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20170411 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20171101 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20180702 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20181120 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20191212 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20210708 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20230816 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcSource VMCv20240226 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vmcdeepSource VMCDEEPv20230713 Error in point source profile-fitted J mag real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jPsfMagErr vvvPsfDaophotJKsSource VVVDR5 Error on PSF magnitude in J band {catalogue TType keyword: J_err} real 4 mag -0.9999995e9 stat.error;instr.det.psf;phot.mag;em.IR.J
jPsfMagErr vvvPsfDophotZYJHKsSource VVVDR5 Error on mean PSF magnitude in J band {catalogue TType keyword: er_J} real 4 mag -0.9999995e9 stat.error;instr.det.psf;em.IR.J
Jsep vvvProperMotionCatalogue VVVDR5 Sky distance between VVV DR4 J detection and the projected source position at the J observation epoch taking the pipeline proper motion into account. {catalogue TType keyword: Jsep} real 4 arcsec -999999500.0  
jSeqNum ultravistaSource ULTRAVISTADR4 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSDR1 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vhsSource VHSDR2 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vhsSource VHSDR3 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSDR4 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSDR5 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSDR6 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSv20120926 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vhsSource VHSv20130417 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vhsSource VHSv20140409 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSv20150108 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSv20160114 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSv20160507 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSv20170630 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSv20180419 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vhsSource VHSv20201209 the running number of the J detection int 4   -99999999 meta.id;em.IR.J
jSeqNum vhsSourceRemeasurement VHSDR1 the running number of the J remeasurement int 4   -99999999 meta.id
jSeqNum videoSource VIDEODR2 the running number of the J detection int 4   -99999999 meta.id
jSeqNum videoSource VIDEODR3 the running number of the J detection int 4   -99999999 meta.number
jSeqNum videoSource VIDEODR4 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum videoSource VIDEODR5 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum videoSource VIDEOv20100513 the running number of the J detection int 4   -99999999 meta.id
jSeqNum videoSource VIDEOv20111208 the running number of the J detection int 4   -99999999 meta.id
jSeqNum videoSourceRemeasurement VIDEOv20100513 the running number of the J remeasurement int 4   -99999999 meta.id
jSeqNum vikingSource VIKINGDR2 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vikingSource VIKINGDR3 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vikingSource VIKINGDR4 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vikingSource VIKINGv20110714 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vikingSource VIKINGv20111019 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vikingSource VIKINGv20130417 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vikingSource VIKINGv20140402 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vikingSource VIKINGv20150421 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vikingSource VIKINGv20151230 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vikingSource VIKINGv20160406 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vikingSource VIKINGv20161202 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vikingSource VIKINGv20170715 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vikingSourceRemeasurement VIKINGv20110714 the running number of the J remeasurement int 4   -99999999 meta.id
jSeqNum vikingSourceRemeasurement VIKINGv20111019 the running number of the J remeasurement int 4   -99999999 meta.id
jSeqNum vmcSource VMCDR2 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vmcSource VMCDR3 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCDR4 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCDR5 the running number of the J detection int 4   -99999999 meta.id;em.IR.J
jSeqNum vmcSource VMCv20110816 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vmcSource VMCv20110909 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vmcSource VMCv20120126 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vmcSource VMCv20121128 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vmcSource VMCv20130304 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vmcSource VMCv20130805 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vmcSource VMCv20140428 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20140903 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20150309 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20151218 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20160311 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20160822 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20170109 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20170411 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20171101 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20180702 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20181120 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vmcSource VMCv20191212 the running number of the J detection int 4   -99999999 meta.id;em.IR.J
jSeqNum vmcSource VMCv20210708 the running number of the J detection int 4   -99999999 meta.id;em.IR.J
jSeqNum vmcSource VMCv20230816 the running number of the J detection int 4   -99999999 meta.id;em.IR.J
jSeqNum vmcSource VMCv20240226 the running number of the J detection int 4   -99999999 meta.id;em.IR.J
jSeqNum vmcSource, vmcSynopticSource VMCDR1 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vmcSourceRemeasurement VMCv20110816 the running number of the J remeasurement int 4   -99999999 meta.id
jSeqNum vmcSourceRemeasurement VMCv20110909 the running number of the J remeasurement int 4   -99999999 meta.id
jSeqNum vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 the running number of the J detection int 4   -99999999 meta.id;em.IR.J
jSeqNum vvvSource VVVDR2 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vvvSource VVVDR5 the running number of the J detection int 4   -99999999 meta.number;em.IR.J
jSeqNum vvvSource VVVv20100531 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vvvSource VVVv20110718 the running number of the J detection int 4   -99999999 meta.id
jSeqNum vvvSource, vvvSynopticSource VVVDR1 the running number of the J detection int 4   -99999999 meta.number
jSeqNum vvvSourceRemeasurement VVVv20100531 the running number of the J remeasurement int 4   -99999999 meta.id
jSeqNum vvvSourceRemeasurement VVVv20110718 the running number of the J remeasurement int 4   -99999999 meta.id
jSerMag2D vhsSource VHSDR1 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vhsSource VHSDR2 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vhsSource VHSDR3 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSDR4 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSDR5 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSDR6 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSv20120926 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vhsSource VHSv20130417 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vhsSource VHSv20140409 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSv20150108 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSv20160114 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSv20160507 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSv20170630 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSv20180419 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vhsSource VHSv20201209 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D videoSource VIDEOv20100513 Not available in SE output real 4 mag -0.9999995e9 phot.mag
jSerMag2D vikingSource VIKINGDR2 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vikingSource VIKINGDR3 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vikingSource VIKINGDR4 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vikingSource VIKINGv20110714 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vikingSource VIKINGv20111019 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vikingSource VIKINGv20130417 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vikingSource VIKINGv20140402 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vikingSource VIKINGv20150421 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vikingSource VIKINGv20151230 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vikingSource VIKINGv20160406 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vikingSource VIKINGv20161202 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vikingSource VIKINGv20170715 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCDR1 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vmcSource VMCDR2 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCDR3 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCDR4 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCDR5 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20110816 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vmcSource VMCv20110909 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vmcSource VMCv20120126 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vmcSource VMCv20121128 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vmcSource VMCv20130304 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag
jSerMag2D vmcSource VMCv20130805 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20140428 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20140903 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20150309 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20151218 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20160311 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20160822 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20170109 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20170411 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20171101 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20180702 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20181120 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20191212 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20210708 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20230816 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcSource VMCv20240226 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2D vmcdeepSource VMCDEEPv20230713 Extended source J mag (profile-fitted) real 4 mag -0.9999995e9 phot.mag;em.IR.J
jSerMag2DErr vhsSource VHSDR1 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vhsSource VHSDR2 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vhsSource VHSDR3 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J
jSerMag2DErr vhsSource VHSDR4 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jSerMag2DErr vhsSource VHSDR5 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vhsSource VHSDR6 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vhsSource VHSv20120926 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vhsSource VHSv20130417 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vhsSource VHSv20140409 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J
jSerMag2DErr vhsSource VHSv20150108 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jSerMag2DErr vhsSource VHSv20160114 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vhsSource VHSv20160507 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vhsSource VHSv20170630 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vhsSource VHSv20180419 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vhsSource VHSv20201209 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr videoSource VIDEOv20100513 Not available in SE output real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vikingSource VIKINGDR2 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vikingSource VIKINGDR3 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vikingSource VIKINGDR4 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J
jSerMag2DErr vikingSource VIKINGv20110714 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vikingSource VIKINGv20111019 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vikingSource VIKINGv20130417 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vikingSource VIKINGv20140402 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vikingSource VIKINGv20150421 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jSerMag2DErr vikingSource VIKINGv20151230 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vikingSource VIKINGv20160406 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vikingSource VIKINGv20161202 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vikingSource VIKINGv20170715 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCDR1 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCDR2 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCDR3 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jSerMag2DErr vmcSource VMCDR4 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCDR5 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20110816 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCv20110909 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCv20120126 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCv20121128 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCv20130304 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCv20130805 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error
jSerMag2DErr vmcSource VMCv20140428 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J
jSerMag2DErr vmcSource VMCv20140903 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jSerMag2DErr vmcSource VMCv20150309 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;em.IR.J;phot.mag
jSerMag2DErr vmcSource VMCv20151218 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20160311 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20160822 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20170109 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20170411 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20171101 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20180702 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20181120 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20191212 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20210708 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20230816 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcSource VMCv20240226 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSerMag2DErr vmcdeepSource VMCDEEPv20230713 Error in extended source J mag (profile-fitted) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.J
jSharp vmcPsfCatalogue VMCDR3 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCDR4 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20121128 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param
jSharp vmcPsfCatalogue VMCv20140428 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20140903 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20150309 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20151218 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20160311 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20160822 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20170109 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20170411 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfCatalogue VMCv20171101 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfDetections VMCv20181120 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jSharp vmcPsfDetections, vmcPsfSource VMCv20180702 PSF fitting shape parameter J filter (ratio of height of bivariate delta function to height of bivariate Gaussian function) {catalogue TType keyword: J_SHARP} real 4 dex -0.9999995e9 stat.fit.param;em.IR.J
jskewness ultravistaMapLcVariability ULTRAVISTADR4 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness ultravistaVariability ULTRAVISTADR4 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness videoVariability VIDEODR2 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness videoVariability VIDEODR3 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness videoVariability VIDEODR4 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness videoVariability VIDEODR5 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness videoVariability VIDEOv20100513 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness videoVariability VIDEOv20111208 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGDR2 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGDR3 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGDR4 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20110714 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20111019 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20130417 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20140402 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20150421 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20151230 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20160406 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20161202 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vikingVariability VIKINGv20170715 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCDR1 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCDR2 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCDR3 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCDR4 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCDR5 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20110816 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20110909 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20120126 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20121128 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20130304 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20130805 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.NIR
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20140428 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20140903 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20150309 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20151218 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20160311 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20160822 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20170109 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20170411 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20171101 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20180702 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20181120 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20191212 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20210708 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20230816 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcVariability VMCv20240226 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vmcdeepVariability VMCDEEPv20230713 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vvvVariability VVVDR5 Skewness in J band (see Sesar et al. 2007) real 4 mag -0.9999995e9 stat.param;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jskewness vvvVariability VVVv20100531 Skewness in J band (see Sesar et al. 2007) real 4   -0.9999995e9  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jtotalPeriod ultravistaMapLcVariability ULTRAVISTADR4 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod ultravistaVariability ULTRAVISTADR4 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod videoVariability VIDEODR2 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod videoVariability VIDEODR3 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod videoVariability VIDEODR4 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod videoVariability VIDEODR5 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod videoVariability VIDEOv20100513 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod videoVariability VIDEOv20111208 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGDR2 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGDR3 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGDR4 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20110714 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20111019 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20130417 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20140402 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20150421 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20151230 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20160406 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20161202 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vikingVariability VIKINGv20170715 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCDR1 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCDR2 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCDR3 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCDR4 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCDR5 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20110816 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20110909 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20120126 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20121128 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20130304 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20130805 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20140428 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration;em.IR.J
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20140903 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20150309 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20151218 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20160311 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20160822 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20170109 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20170411 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20171101 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20180702 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20181120 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20191212 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20210708 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20230816 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcVariability VMCv20240226 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vmcdeepVariability VMCDEEPv20230713 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vvvVariability VVVDR5 total period of observations (last obs-first obs) real 4 days -0.9999995e9 time.duration
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
jtotalPeriod vvvVariability VVVv20100531 total period of observations (last obs-first obs) real 4 days -0.9999995e9  
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
Julian denisDR3Source DENIS Julian day for DENIS observation real 4 JD    
julianDayNum Multiframe SHARKSv20210222 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe SHARKSv20210421 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe ULTRAVISTADR4 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSDR1 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSDR2 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSDR3 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSDR4 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSDR5 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSDR6 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20120926 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20130417 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20140409 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20150108 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20160114 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20160507 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20170630 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20180419 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VHSv20201209 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIDEODR2 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIDEODR3 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIDEODR4 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIDEODR5 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIDEOv20100513 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIDEOv20111208 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGDR2 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGDR3 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGDR4 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20110714 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20111019 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20130417 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20140402 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20150421 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20151230 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20160406 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20161202 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VIKINGv20170715 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCDEEPv20230713 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCDR1 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCDR2 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCDR3 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCDR4 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCDR5 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20110816 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20110909 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20120126 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20121128 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20130304 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20130805 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20140428 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20140903 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20150309 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20151218 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20160311 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20160822 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20170109 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20170411 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20171101 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20180702 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20181120 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20191212 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20210708 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20230816 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VMCv20240226 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VVVDR1 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VVVDR2 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VVVDR5 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VVVXDR1 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VVVv20100531 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum Multiframe VVVv20110718 The Julian Day number of the VISTA night int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector SHARKSv20210222 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector SHARKSv20210421 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector ULTRAVISTADR4 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSDR1 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSDR2 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSDR3 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSDR4 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSDR5 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSDR6 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20120926 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20130417 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20140409 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20150108 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20160114 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20160507 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20170630 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20180419 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VHSv20201209 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIDEODR2 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIDEODR3 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIDEODR4 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIDEODR5 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIDEOv20100513 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIDEOv20111208 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGDR2 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGDR3 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGDR4 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20110714 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20111019 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20130417 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20140402 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20150421 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20151230 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20160406 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20161202 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VIKINGv20170715 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCDEEPv20230713 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCDR1 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCDR2 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCDR3 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCDR4 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCDR5 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20110816 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20110909 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20120126 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20121128 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20130304 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20130805 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20140428 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20140903 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20150309 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20151218 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20160311 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20160822 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20170109 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20170411 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20171101 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20180702 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20181120 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20191212 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20210708 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20230816 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VMCv20240226 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VVVDR1 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VVVDR2 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VVVDR5 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VVVXDR1 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VVVv20100531 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum MultiframeDetector VVVv20110718 The Julian Day number of the VISTA night {image primary HDU keyword: DATE-OBS} int 4 Julian days -99999999 time.epoch
julianDayNum sharksMultiframe, sharksMultiframeDetector, ultravistaMultiframe, ultravistaMultiframeDetector, vhsMultiframe, vhsMultiframeDetector, videoMultiframe, videoMultiframeDetector, vikingMultiframe, vikingMultiframeDetector, vmcMultiframe, vmcMultiframeDetector, vvvMultiframe, vvvMultiframeDetector VSAQC The Julian Day number of the VISTA night int 4 Julian days time.epoch
jVarClass ultravistaMapLcVariability ULTRAVISTADR4 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass ultravistaVariability ULTRAVISTADR4 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass videoVariability VIDEODR2 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass videoVariability VIDEODR3 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass videoVariability VIDEODR4 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass videoVariability VIDEODR5 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass videoVariability VIDEOv20100513 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass videoVariability VIDEOv20111208 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGDR2 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGDR3 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGDR4 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20110714 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20111019 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20130417 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20140402 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20150421 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20151230 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20160406 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20161202 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vikingVariability VIKINGv20170715 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCDR1 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCDR2 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCDR3 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCDR4 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCDR5 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20110816 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20110909 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20120126 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20121128 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20130304 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20130805 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20140428 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20140903 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20150309 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20151218 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20160311 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20160822 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20170109 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20170411 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20171101 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20180702 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20181120 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20191212 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20210708 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20230816 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcVariability VMCv20240226 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vmcdeepVariability VMCDEEPv20230713 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vvvVariability VVVDR5 Classification of variability in this band smallint 2   -9999 meta.code.class;src.var;em.IR.J
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jVarClass vvvVariability VVVv20100531 Classification of variability in this band smallint 2   -9999  
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
jXi ultravistaSource ULTRAVISTADR4 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSDR1 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSDR2 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSDR3 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSDR4 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSDR5 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSDR6 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20120926 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20130417 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20140409 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20150108 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20160114 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20160507 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20170630 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20180419 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vhsSource VHSv20201209 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi videoSource VIDEODR2 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi videoSource VIDEODR3 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi videoSource VIDEODR4 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi videoSource VIDEODR5 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi videoSource VIDEOv20100513 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi videoSource VIDEOv20111208 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGDR2 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGDR3 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGDR4 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20110714 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20111019 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20130417 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20140402 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20150421 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20151230 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20160406 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20161202 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vikingSource VIKINGv20170715 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCDR2 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCDR3 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCDR4 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCDR5 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20110816 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20110909 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20120126 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20121128 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20130304 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20130805 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20140428 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20140903 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20150309 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20151218 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20160311 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20160822 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20170109 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20170411 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20171101 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20180702 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20181120 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20191212 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20210708 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20230816 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource VMCv20240226 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcSource, vmcSynopticSource VMCDR1 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vvvSource VVVDR2 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vvvSource VVVDR5 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff;em.IR.J
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vvvSource VVVv20100531 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vvvSource VVVv20110718 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
jXi vvvSource, vvvSynopticSource VVVDR1 Offset of J detection from master position (+east/-west) real 4 arcsec -0.9999995e9 pos.eq.ra;arith.diff
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 1.0 arcseconds is used. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the VHS, you might wish to insist that the offsets in the selected sample are all below 0.5 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.



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04/03/2024