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Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20181120 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20181120 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

Y

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified Content Descriptor
y allwise_sc2 WISE Unit sphere position y value float 8      
y combo17CDFSSource COMBO17 y-coordinate on image cdfs_r.fit real 4 pix    
y vhsDetection VHSDR2 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSDR3 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSDR4 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20120926 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20130417 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20140409 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20150108 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20160114 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20160507 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20170630 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20171207 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection VHSv20180419 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vhsDetection, vhsListRemeasurement VHSDR1 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y videoDetection VIDEODR2 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y videoDetection VIDEODR3 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y videoDetection VIDEODR4 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y videoDetection VIDEODR5 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y videoDetection VIDEOv20100513 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y videoDetection VIDEOv20111208 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y videoListRemeasurement VIDEOv20100513 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGDR2 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGDR3 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGDR4 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20111019 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20130417 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20140402 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20150421 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20151230 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20160406 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20161202 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20170715 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection VIKINGv20181012 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingDetection, vikingListRemeasurement VIKINGv20110714 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingMapRemeasurement VIKINGZYSELJv20160909 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vikingMapRemeasurement VIKINGZYSELJv20170124 Y coordinate of detection (SE: Y_IMAGE) {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCDR1 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCDR2 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCDR3 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCDR4 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20110909 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20120126 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20121128 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20130304 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20130805 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20140428 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20140903 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20150309 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20151218 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20160311 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20160822 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20170109 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20170411 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20171101 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20180702 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection VMCv20181120 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vmcDetection, vmcListRemeasurement VMCv20110816 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y vvvDetection VVVDR4 Y coordinate of detection {catalogue TType keyword: Y_coordinate}
Intensity-weighted isophotal centre-of-gravity in Y.
real 4 pixels   pos.cartesian.y;instr.plate
y_1eNum vvvPsfDophotZYJHKsMergeLog VVVDR4 the extension number of this 1st epoch Y frame tinyint 1     meta.number;em.IR.NIR
y_1mfID vvvPsfDophotZYJHKsMergeLog VVVDR4 the UID of the relevant 1st epoch Y tile multiframe bigint 8     meta.id;obs.field;em.IR.NIR
y_1Mjd vvvPsfDophotZYJHKsMergeLog VVVDR4 the MJD of the 1st epoch Y tile multiframe float 8     time;em.IR.NIR
y_2eNum vvvPsfDophotZYJHKsMergeLog VVVDR4 the extension number of this 2nd epoch Y frame tinyint 1     meta.number;em.IR.NIR
y_2mfID vvvPsfDophotZYJHKsMergeLog VVVDR4 the UID of the relevant 2nd epoch Y tile multiframe bigint 8     meta.id;obs.field;em.IR.NIR
y_2Mjd vvvPsfDophotZYJHKsMergeLog VVVDR4 the MJD of the 2nd epoch Y tile multiframe float 8     time;em.IR.NIR
Y_BJ grs_ngpSource, grs_ranSource, grs_sgpSource TWODFGRS Plate y_bj in 8 micron pixels real 4      
y_coadd twomass_xsc TWOMASS y (in-scan) position (coadd coord.). real 4 arcsec   pos.cartesian;instr.det
Y_IMAGE mgcDetection MGC Object y position real 4 pixel    
Y_OFF mgcGalaxyStruct MGC Y offset of Galaxy Centre real 4   99.99  
Y_OFFm mgcGalaxyStruct MGC Y offset error (-) real 4   99.99  
Y_OFFp mgcGalaxyStruct MGC Y offset error (+) real 4   99.99  
Y_R spectra SIXDF y position of object from R frame int 4      
Y_V spectra SIXDF y position of object from V frame int 4      
yAmpl vmcCepheidVariables VMCDR4 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmpl vmcCepheidVariables VMCv20160311 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmpl vmcCepheidVariables VMCv20160822 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmpl vmcCepheidVariables VMCv20170109 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmpl vmcCepheidVariables VMCv20170411 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmpl vmcCepheidVariables VMCv20171101 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmpl vmcCepheidVariables VMCv20180702 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmpl vmcCepheidVariables VMCv20181120 Peak-to-Peak amplitude in Y band {catalogue TType keyword: A(Y)} real 4 mag -0.9999995e9 src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCDR4 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCv20160311 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCv20160822 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCv20170109 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCv20170411 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCv20171101 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCv20180702 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAmplErr vmcCepheidVariables VMCv20181120 Error in Peak-to-Peak amplitude in Y band {catalogue TType keyword: e_A(Y)} real 4 mag -0.9999995e9 stat.error;src.var.amplitude;em.IR.NIR
yAperJky3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source Y aperture corrected (2.0 arcsec aperture diameter) calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
yAperJky3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source Y aperture corrected (2.0 arcsec aperture diameter) calibrated flux
If in doubt use this flux estimator
real 4 jansky -0.9999995e9 phot.flux
yAperJky3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source Y (2.0 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
yAperJky3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source Y (2.0 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
yAperJky4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source Y aperture corrected (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperJky4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source Y aperture corrected (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperJky4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source Y (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
yAperJky4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source Y (2.8 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
yAperJky6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source Y aperture corrected (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperJky6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source Y aperture corrected (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperJky6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source Y (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
yAperJky6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source Y (5.7 arcsec aperture diameter) calibrated flux real 4 jansky -0.9999995e9 stat.error
yAperJkyNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source Y (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
yAperJkyNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source Y (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
yAperJkyNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperJkyNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperJkyNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperJkyNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture calibrated flux real 4 jansky -0.9999995e9 phot.flux
yAperLup3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source Y aperture corrected (2.0 arcsec aperture diameter) luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
yAperLup3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source Y aperture corrected (2.0 arcsec aperture diameter) luptitude
If in doubt use this flux estimator
real 4 lup -0.9999995e9 phot.lup
yAperLup3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source Y (2.0 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
yAperLup3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source Y (2.0 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
yAperLup4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source Y aperture corrected (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
yAperLup4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source Y aperture corrected (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
yAperLup4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source Y (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
yAperLup4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source Y (2.8 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
yAperLup6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source Y aperture corrected (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
yAperLup6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source Y aperture corrected (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 phot.lup
yAperLup6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source Y (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
yAperLup6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source Y (5.7 arcsec aperture diameter) luptitude real 4 lup -0.9999995e9 stat.error
yAperLupNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source Y (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
yAperLupNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source Y (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
yAperLupNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
yAperLupNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
yAperLupNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
yAperLupNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture luptitude real 4 lup -0.9999995e9 phot.lup
yAperMag1 vmcSynopticSource VMCDR1 Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag1 vmcSynopticSource VMCDR2 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCDR3 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCDR4 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20110816 Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag1 vmcSynopticSource VMCv20110909 Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag1 vmcSynopticSource VMCv20120126 Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag1 vmcSynopticSource VMCv20121128 Extended source Y aperture corrected mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag1 vmcSynopticSource VMCv20130304 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag1 vmcSynopticSource VMCv20130805 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20140428 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20140903 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20150309 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20151218 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20160311 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20160822 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20170109 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20170411 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20171101 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20180702 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vmcSynopticSource VMCv20181120 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vvvSource VVVDR4 Point source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1 vvvSynopticSource VVVDR4 Extended source Y aperture corrected mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCDR1 Error in extended source Y mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCDR2 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCDR3 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag1Err vmcSynopticSource VMCDR4 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20110816 Error in extended source Y mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCv20110909 Error in extended source Y mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCv20120126 Error in extended source Y mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCv20121128 Error in extended source Y mag (0.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCv20130304 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCv20130805 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag1Err vmcSynopticSource VMCv20140428 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20140903 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag1Err vmcSynopticSource VMCv20150309 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag1Err vmcSynopticSource VMCv20151218 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20160311 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20160822 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20170109 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20170411 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20171101 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20180702 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vmcSynopticSource VMCv20181120 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vvvSource VVVDR4 Error in point source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag1Err vvvSynopticSource VVVDR4 Error in extended source Y mag (1.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCDR1 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag2 vmcSynopticSource VMCDR2 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCDR3 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCDR4 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20110816 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag2 vmcSynopticSource VMCv20110909 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag2 vmcSynopticSource VMCv20120126 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag2 vmcSynopticSource VMCv20121128 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag2 vmcSynopticSource VMCv20130304 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag2 vmcSynopticSource VMCv20130805 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20140428 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20140903 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20150309 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20151218 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20160311 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20160822 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20170109 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20170411 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20171101 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20180702 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vmcSynopticSource VMCv20181120 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2 vvvSynopticSource VVVDR4 Extended source Y aperture corrected mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCDR1 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCDR2 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCDR3 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag2Err vmcSynopticSource VMCDR4 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20110816 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCv20110909 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCv20120126 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCv20121128 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCv20130304 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCv20130805 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag2Err vmcSynopticSource VMCv20140428 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20140903 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag2Err vmcSynopticSource VMCv20150309 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag2Err vmcSynopticSource VMCv20151218 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20160311 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20160822 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20170109 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20170411 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20171101 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20180702 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vmcSynopticSource VMCv20181120 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag2Err vvvSynopticSource VVVDR4 Error in extended source Y mag (1.4 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSDR1 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vhsSource VHSDR2 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vhsSource VHSDR3 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSDR4 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSv20120926 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vhsSource VHSv20130417 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vhsSource VHSv20140409 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSv20150108 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSv20160114 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSv20160507 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSv20170630 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSv20171207 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vhsSource VHSv20180419 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 videoSource VIDEODR2 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 videoSource VIDEODR3 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 videoSource VIDEODR4 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 videoSource VIDEODR5 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 videoSource VIDEOv20100513 Default point/extended source Y mag, no aperture correction applied
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 videoSource VIDEOv20111208 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vikingSource VIKINGDR2 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vikingSource VIKINGDR3 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vikingSource VIKINGDR4 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingSource VIKINGv20110714 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vikingSource VIKINGv20111019 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vikingSource VIKINGv20130417 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vikingSource VIKINGv20140402 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingSource VIKINGv20150421 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingSource VIKINGv20151230 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingSource VIKINGv20160406 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingSource VIKINGv20161202 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingSource VIKINGv20170715 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingSource VIKINGv20181012 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default point source Y aperture corrected (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default point source Y aperture corrected (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSource VMCDR1 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSource VMCDR2 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCDR3 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCDR4 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20110816 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSource VMCv20110909 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSource VMCv20120126 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSource VMCv20121128 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSource VMCv20130304 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSource VMCv20130805 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20140428 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20140903 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20150309 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20151218 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20160311 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20160822 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20170109 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20170411 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20171101 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20180702 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSource VMCv20181120 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCDR1 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSynopticSource VMCDR2 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCDR3 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCDR4 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20110816 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSynopticSource VMCv20110909 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSynopticSource VMCv20120126 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSynopticSource VMCv20121128 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSynopticSource VMCv20130304 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag3 vmcSynopticSource VMCv20130805 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20140428 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20140903 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20150309 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20151218 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20160311 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20160822 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20170109 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20170411 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20171101 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20180702 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vmcSynopticSource VMCv20181120 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vvvSource VVVDR4 Default point source Y aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3 vvvSynopticSource VVVDR4 Default point/extended source Y aperture corrected mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag3Err vhsSource VHSDR1 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vhsSource VHSDR2 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vhsSource VHSDR3 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag3Err vhsSource VHSDR4 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err vhsSource VHSv20120926 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vhsSource VHSv20130417 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vhsSource VHSv20140409 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag3Err vhsSource VHSv20150108 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err vhsSource VHSv20160114 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vhsSource VHSv20160507 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vhsSource VHSv20170630 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vhsSource VHSv20171207 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vhsSource VHSv20180419 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err videoSource VIDEODR2 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err videoSource VIDEODR3 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err videoSource VIDEODR4 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err videoSource VIDEODR5 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err videoSource VIDEOv20100513 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err videoSource VIDEOv20111208 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingSource VIKINGDR2 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingSource VIKINGDR3 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingSource VIKINGDR4 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag3Err vikingSource VIKINGv20110714 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingSource VIKINGv20111019 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingSource VIKINGv20130417 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingSource VIKINGv20140402 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingSource VIKINGv20150421 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err vikingSource VIKINGv20151230 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vikingSource VIKINGv20160406 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vikingSource VIKINGv20161202 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vikingSource VIKINGv20170715 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vikingSource VIKINGv20181012 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in default point/extended source Y (2.0 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
yAperMag3Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in default point/extended source Y (2.0 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCDR2 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCDR3 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err vmcSource VMCDR4 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20110816 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCv20110909 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCv20120126 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCv20121128 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCv20130304 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCv20130805 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vmcSource VMCv20140428 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag3Err vmcSource VMCv20140903 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err vmcSource VMCv20150309 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag3Err vmcSource VMCv20151218 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20160311 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20160822 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20170109 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20170411 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20171101 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20180702 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource VMCv20181120 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vmcSource, vmcSynopticSource VMCDR1 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag3Err vvvSource VVVDR4 Error in default point source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag3Err vvvSynopticSource VVVDR4 Error in default point/extended source Y mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSDR1 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vhsSource VHSDR2 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vhsSource VHSDR3 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSDR4 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSv20120926 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vhsSource VHSv20130417 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vhsSource VHSv20140409 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSv20150108 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSv20160114 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSv20160507 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSv20170630 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSv20171207 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vhsSource VHSv20180419 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 videoSource VIDEODR2 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 videoSource VIDEODR3 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 videoSource VIDEODR4 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 videoSource VIDEODR5 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 videoSource VIDEOv20100513 Extended source Y mag, no aperture correction applied real 4 mag -0.9999995e9 phot.mag
yAperMag4 videoSource VIDEOv20111208 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vikingSource VIKINGDR2 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vikingSource VIKINGDR3 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vikingSource VIKINGDR4 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingSource VIKINGv20110714 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vikingSource VIKINGv20111019 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vikingSource VIKINGv20130417 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vikingSource VIKINGv20140402 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingSource VIKINGv20150421 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingSource VIKINGv20151230 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingSource VIKINGv20160406 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingSource VIKINGv20161202 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingSource VIKINGv20170715 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingSource VIKINGv20181012 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source Y aperture corrected (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source Y aperture corrected (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSource VMCDR1 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSource VMCDR2 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCDR3 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCDR4 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20110816 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSource VMCv20110909 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSource VMCv20120126 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSource VMCv20121128 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSource VMCv20130304 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSource VMCv20130805 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20140428 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20140903 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20150309 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20151218 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20160311 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20160822 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20170109 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20170411 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20171101 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20180702 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSource VMCv20181120 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCDR1 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSynopticSource VMCDR2 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCDR3 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCDR4 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20110816 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSynopticSource VMCv20110909 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSynopticSource VMCv20120126 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSynopticSource VMCv20121128 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSynopticSource VMCv20130304 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag4 vmcSynopticSource VMCv20130805 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20140428 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20140903 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20150309 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20151218 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20160311 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20160822 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20170109 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20170411 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20171101 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20180702 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vmcSynopticSource VMCv20181120 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vvvSource VVVDR4 Point source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4 vvvSynopticSource VVVDR4 Extended source Y aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag4Err vhsSource VHSDR1 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vhsSource VHSDR2 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vhsSource VHSDR3 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag4Err vhsSource VHSDR4 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vhsSource VHSv20120926 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vhsSource VHSv20130417 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vhsSource VHSv20140409 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag4Err vhsSource VHSv20150108 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vhsSource VHSv20160114 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vhsSource VHSv20160507 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vhsSource VHSv20170630 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vhsSource VHSv20171207 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vhsSource VHSv20180419 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err videoSource VIDEODR2 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err videoSource VIDEODR3 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err videoSource VIDEODR4 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err videoSource VIDEODR5 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err videoSource VIDEOv20100513 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err videoSource VIDEOv20111208 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingSource VIKINGDR2 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingSource VIKINGDR3 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingSource VIKINGDR4 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag4Err vikingSource VIKINGv20110714 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingSource VIKINGv20111019 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingSource VIKINGv20130417 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingSource VIKINGv20140402 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingSource VIKINGv20150421 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vikingSource VIKINGv20151230 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vikingSource VIKINGv20160406 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vikingSource VIKINGv20161202 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vikingSource VIKINGv20170715 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vikingSource VIKINGv20181012 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source Y (2.8 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
yAperMag4Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source Y (2.8 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCDR1 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCDR2 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCDR3 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vmcSource VMCDR4 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20110816 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCv20110909 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCv20120126 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCv20121128 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCv20130304 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCv20130805 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSource VMCv20140428 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag4Err vmcSource VMCv20140903 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vmcSource VMCv20150309 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vmcSource VMCv20151218 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20160311 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20160822 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20170109 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20170411 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20171101 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20180702 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSource VMCv20181120 Error in point/extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCDR1 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCDR2 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCDR3 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vmcSynopticSource VMCDR4 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20110816 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCv20110909 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCv20120126 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCv20121128 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCv20130304 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCv20130805 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag4Err vmcSynopticSource VMCv20140428 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20140903 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vmcSynopticSource VMCv20150309 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag4Err vmcSynopticSource VMCv20151218 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20160311 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20160822 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20170109 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20170411 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20171101 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20180702 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vmcSynopticSource VMCv20181120 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vvvSource VVVDR4 Error in point source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag4Err vvvSynopticSource VVVDR4 Error in extended source Y mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCDR1 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag5 vmcSynopticSource VMCDR2 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCDR3 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCDR4 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20110816 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag5 vmcSynopticSource VMCv20110909 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag5 vmcSynopticSource VMCv20120126 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag5 vmcSynopticSource VMCv20121128 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag5 vmcSynopticSource VMCv20130304 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag5 vmcSynopticSource VMCv20130805 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20140428 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20140903 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20150309 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20151218 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20160311 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20160822 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20170109 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20170411 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20171101 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20180702 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vmcSynopticSource VMCv20181120 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5 vvvSynopticSource VVVDR4 Extended source Y aperture corrected mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCDR1 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCDR2 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCDR3 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag5Err vmcSynopticSource VMCDR4 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20110816 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCv20110909 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCv20120126 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCv20121128 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCv20130304 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCv20130805 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag5Err vmcSynopticSource VMCv20140428 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20140903 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag5Err vmcSynopticSource VMCv20150309 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag5Err vmcSynopticSource VMCv20151218 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20160311 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20160822 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20170109 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20170411 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20171101 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20180702 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vmcSynopticSource VMCv20181120 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag5Err vvvSynopticSource VVVDR4 Error in extended source Y mag (4.0 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSDR1 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vhsSource VHSDR2 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vhsSource VHSDR3 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSDR4 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSv20120926 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vhsSource VHSv20130417 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vhsSource VHSv20140409 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSv20150108 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSv20160114 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSv20160507 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSv20170630 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSv20171207 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vhsSource VHSv20180419 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 videoSource VIDEODR2 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 videoSource VIDEODR3 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 videoSource VIDEODR4 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 videoSource VIDEODR5 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 videoSource VIDEOv20100513 Extended source Y mag, no aperture correction applied real 4 mag -0.9999995e9 phot.mag
yAperMag6 videoSource VIDEOv20111208 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vikingSource VIKINGDR2 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vikingSource VIKINGDR3 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vikingSource VIKINGDR4 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingSource VIKINGv20110714 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vikingSource VIKINGv20111019 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vikingSource VIKINGv20130417 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vikingSource VIKINGv20140402 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingSource VIKINGv20150421 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingSource VIKINGv20151230 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingSource VIKINGv20160406 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingSource VIKINGv20161202 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingSource VIKINGv20170715 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingSource VIKINGv20181012 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Point source Y aperture corrected (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Point source Y aperture corrected (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vmcSource VMCDR1 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vmcSource VMCDR2 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCDR3 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCDR4 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20110816 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vmcSource VMCv20110909 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vmcSource VMCv20120126 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vmcSource VMCv20121128 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vmcSource VMCv20130304 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMag6 vmcSource VMCv20130805 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20140428 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20140903 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20150309 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20151218 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20160311 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20160822 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20170109 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20170411 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20171101 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20180702 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6 vmcSource VMCv20181120 Point source Y aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMag6Err vhsSource VHSDR1 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vhsSource VHSDR2 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vhsSource VHSDR3 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag6Err vhsSource VHSDR4 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err vhsSource VHSv20120926 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vhsSource VHSv20130417 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vhsSource VHSv20140409 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag6Err vhsSource VHSv20150108 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err vhsSource VHSv20160114 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vhsSource VHSv20160507 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vhsSource VHSv20170630 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vhsSource VHSv20171207 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vhsSource VHSv20180419 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err videoSource VIDEODR2 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err videoSource VIDEODR3 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err videoSource VIDEODR4 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err videoSource VIDEODR5 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err videoSource VIDEOv20100513 Error in extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err videoSource VIDEOv20111208 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingSource VIKINGDR2 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingSource VIKINGDR3 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingSource VIKINGDR4 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag6Err vikingSource VIKINGv20110714 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingSource VIKINGv20111019 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingSource VIKINGv20130417 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingSource VIKINGv20140402 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingSource VIKINGv20150421 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err vikingSource VIKINGv20151230 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vikingSource VIKINGv20160406 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vikingSource VIKINGv20161202 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vikingSource VIKINGv20170715 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vikingSource VIKINGv20181012 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Error in point/extended source Y (5.7 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
yAperMag6Err vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Error in point/extended source Y (5.7 arcsec aperture diameter) magnitude real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCDR1 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCDR2 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCDR3 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err vmcSource VMCDR4 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20110816 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCv20110909 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCv20120126 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCv20121128 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCv20130304 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCv20130805 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error
yAperMag6Err vmcSource VMCv20140428 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR
yAperMag6Err vmcSource VMCv20140903 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err vmcSource VMCv20150309 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yAperMag6Err vmcSource VMCv20151218 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20160311 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20160822 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20170109 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20170411 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20171101 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20180702 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMag6Err vmcSource VMCv20181120 Error in point/extended source Y mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSDR1 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vhsSource VHSDR2 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vhsSource VHSDR3 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSDR4 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSv20120926 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vhsSource VHSv20130417 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vhsSource VHSv20140409 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSv20150108 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSv20160114 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSv20160507 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSv20170630 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSv20171207 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vhsSource VHSv20180419 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 videoSource VIDEODR2 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 videoSource VIDEODR3 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 videoSource VIDEODR4 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 videoSource VIDEODR5 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 videoSource VIDEOv20111208 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vikingSource VIKINGDR2 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vikingSource VIKINGDR3 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vikingSource VIKINGDR4 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingSource VIKINGv20110714 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vikingSource VIKINGv20111019 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vikingSource VIKINGv20130417 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vikingSource VIKINGv20140402 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingSource VIKINGv20150421 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingSource VIKINGv20151230 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingSource VIKINGv20160406 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingSource VIKINGv20161202 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingSource VIKINGv20170715 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingSource VIKINGv20181012 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Default extended source Y (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
yAperMagNoAperCorr3 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Default extended source Y (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
yAperMagNoAperCorr3 vmcSource VMCDR1 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vmcSource VMCDR2 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCDR3 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCDR4 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20110816 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vmcSource VMCv20110909 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vmcSource VMCv20120126 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vmcSource VMCv20121128 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vmcSource VMCv20130304 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr3 vmcSource VMCv20130805 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20140428 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20140903 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20150309 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20151218 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20160311 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20160822 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20170109 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20170411 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20171101 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20180702 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr3 vmcSource VMCv20181120 Default extended source Y aperture mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSDR1 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vhsSource VHSDR2 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vhsSource VHSDR3 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSDR4 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSv20120926 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vhsSource VHSv20130417 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vhsSource VHSv20140409 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSv20150108 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSv20160114 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSv20160507 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSv20170630 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSv20171207 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vhsSource VHSv20180419 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 videoSource VIDEODR2 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 videoSource VIDEODR3 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 videoSource VIDEODR4 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 videoSource VIDEODR5 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 videoSource VIDEOv20111208 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vikingSource VIKINGDR2 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vikingSource VIKINGDR3 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vikingSource VIKINGDR4 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingSource VIKINGv20110714 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vikingSource VIKINGv20111019 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vikingSource VIKINGv20130417 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vikingSource VIKINGv20140402 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingSource VIKINGv20150421 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingSource VIKINGv20151230 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingSource VIKINGv20160406 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingSource VIKINGv20161202 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingSource VIKINGv20170715 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingSource VIKINGv20181012 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source Y (2.8 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vmcSource VMCDR1 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vmcSource VMCDR2 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCDR3 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCDR4 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20110816 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vmcSource VMCv20110909 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vmcSource VMCv20120126 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vmcSource VMCv20121128 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vmcSource VMCv20130304 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr4 vmcSource VMCv20130805 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20140428 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20140903 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20150309 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20151218 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20160311 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20160822 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20170109 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20170411 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20171101 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20180702 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr4 vmcSource VMCv20181120 Extended source Y aperture mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSDR1 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vhsSource VHSDR2 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vhsSource VHSDR3 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSDR4 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSv20120926 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vhsSource VHSv20130417 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vhsSource VHSv20140409 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSv20150108 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSv20160114 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSv20160507 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSv20170630 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSv20171207 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vhsSource VHSv20180419 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 videoSource VIDEODR2 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 videoSource VIDEODR3 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 videoSource VIDEODR4 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 videoSource VIDEODR5 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 videoSource VIDEOv20111208 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vikingSource VIKINGDR2 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vikingSource VIKINGDR3 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vikingSource VIKINGDR4 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingSource VIKINGv20110714 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vikingSource VIKINGv20111019 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vikingSource VIKINGv20130417 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vikingSource VIKINGv20140402 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingSource VIKINGv20150421 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingSource VIKINGv20151230 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingSource VIKINGv20160406 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingSource VIKINGv20161202 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingSource VIKINGv20170715 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingSource VIKINGv20181012 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Extended source Y (5.7 arcsec aperture diameter, but no aperture correction applied) aperture magnitude real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vmcSource VMCDR1 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vmcSource VMCDR2 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCDR3 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCDR4 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20110816 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vmcSource VMCv20110909 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vmcSource VMCv20120126 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vmcSource VMCv20121128 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vmcSource VMCv20130304 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag
yAperMagNoAperCorr6 vmcSource VMCv20130805 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20140428 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20140903 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20150309 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20151218 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20160311 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20160822 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20170109 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20170411 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20171101 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20180702 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yAperMagNoAperCorr6 vmcSource VMCv20181120 Extended source Y aperture mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yaStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, a, in fit to astrometric rms vs magnitude in Y 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.
yaStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band. 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.
yaStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band. 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.
yaStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, a, in fit to photometric rms vs magnitude in Y 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.
yaStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band. 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.
yaStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band. 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.
yAverageConf vhsSource VHSDR1 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vhsSource VHSDR2 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vhsSource VHSDR3 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSDR4 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20120926 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20130417 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20140409 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20150108 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20160114 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20160507 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20170630 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20171207 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vhsSource VHSv20180419 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGDR2 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vikingSource VIKINGDR3 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGDR4 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20110714 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vikingSource VIKINGv20111019 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vikingSource VIKINGv20130417 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20140402 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20150421 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20151230 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20160406 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20161202 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20170715 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingSource VIKINGv20181012 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCDR2 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCDR3 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCDR4 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20110816 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vmcSource VMCv20110909 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vmcSource VMCv20120126 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vmcSource VMCv20121128 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20130304 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20130805 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20140428 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20140903 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20150309 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20151218 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20160311 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20160822 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20170109 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20170411 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20171101 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20180702 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource VMCv20181120 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
yAverageConf vmcSource, vmcSynopticSource VMCDR1 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -99999999 meta.code
yAverageConf vvvSource, vvvSynopticSource VVVDR4 average confidence in 2 arcsec diameter default aperture (aper3) Y real 4   -0.9999995e9 stat.likelihood;em.IR.NIR
YB eros2LMCSource, eros2SMCSource, erosLMCSource, erosSMCSource EROS Y pixel coordinate on blue reference images relative to rebined reference images in [klmn] frame real 4      
ybestAper videoVariability VIDEODR2 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper videoVariability VIDEODR3 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper videoVariability VIDEODR4 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper videoVariability VIDEODR5 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper videoVariability VIDEOv20100513 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper videoVariability VIDEOv20111208 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vikingVariability VIKINGv20110714 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCDR1 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCDR2 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCDR3 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCDR4 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20110816 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20110909 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20120126 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20121128 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20130304 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20130805 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20140428 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20140903 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20150309 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20151218 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20160311 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20160822 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20170109 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20170411 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20171101 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20180702 Best aperture (1-6) for photometric statistics in the Y 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)
ybestAper vmcVariability VMCv20181120 Best aperture (1-6) for photometric statistics in the Y 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)
ybStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, b, in fit to astrometric rms vs magnitude in Y 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.
ybStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band. 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.
ybStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band. 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.
ybStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, b, in fit to photometric rms vs magnitude in Y 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.
ybStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band. 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.
ybStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band. 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.
ychiSqAst videoVarFrameSetInfo VIDEODR2 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst videoVarFrameSetInfo VIDEODR3 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst videoVarFrameSetInfo VIDEODR4 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst videoVarFrameSetInfo VIDEODR5 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst videoVarFrameSetInfo VIDEOv20100513 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst videoVarFrameSetInfo VIDEOv20111208 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vikingVarFrameSetInfo VIKINGv20110714 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCDR1 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCDR2 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCDR3 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCDR4 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20110816 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20110909 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20120126 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20121128 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20130304 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20130805 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20140428 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20140903 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20150309 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20151218 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20160311 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20160822 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20170109 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20170411 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20171101 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20180702 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqAst vmcVarFrameSetInfo VMCv20181120 Goodness of fit of Strateva function to astrometric data in Y 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd videoVariability VIDEODR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd videoVariability VIDEODR5 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd vmcVariability VMCDR3 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCDR4 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd 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.
ychiSqpd vmcVariability VMCv20140428 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20140903 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20150309 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20151218 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20160311 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20160822 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20170109 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20170411 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20171101 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20180702 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqpd vmcVariability VMCv20181120 Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9 stat.fit.chi2;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.
ychiSqPht videoVarFrameSetInfo VIDEODR2 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht videoVarFrameSetInfo VIDEODR3 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht videoVarFrameSetInfo VIDEODR4 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht videoVarFrameSetInfo VIDEODR5 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht videoVarFrameSetInfo VIDEOv20100513 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht videoVarFrameSetInfo VIDEOv20111208 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vikingVarFrameSetInfo VIKINGv20110714 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCDR1 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCDR2 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCDR3 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCDR4 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20110816 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20110909 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20120126 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20121128 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20130304 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20130805 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20140428 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20140903 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20150309 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20151218 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20160311 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20160822 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20170109 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20170411 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20171101 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20180702 Goodness of fit of Strateva function to photometric data in Y 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.
ychiSqPht vmcVarFrameSetInfo VMCv20181120 Goodness of fit of Strateva function to photometric data in Y 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.
Yclass vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR4 VVV DR4 Y 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: Yclass} int 4   -99999999  
yClass vhsSource VHSDR2 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vhsSource VHSDR3 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSDR4 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSv20120926 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vhsSource VHSv20130417 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vhsSource VHSv20140409 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSv20150108 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSv20160114 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSv20160507 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSv20170630 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSv20171207 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource VHSv20180419 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vhsSource, vhsSourceRemeasurement VHSDR1 discrete image classification flag in Y smallint 2   -9999 src.class
yClass videoSource VIDEODR2 discrete image classification flag in Y smallint 2   -9999 src.class
yClass videoSource VIDEODR3 discrete image classification flag in Y smallint 2   -9999 src.class
yClass videoSource VIDEODR4 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass videoSource VIDEODR5 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass videoSource VIDEOv20111208 discrete image classification flag in Y smallint 2   -9999 src.class
yClass videoSource, videoSourceRemeasurement VIDEOv20100513 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingSource VIKINGDR2 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingSource VIKINGDR3 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingSource VIKINGDR4 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vikingSource VIKINGv20111019 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingSource VIKINGv20130417 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingSource VIKINGv20140402 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingSource VIKINGv20150421 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vikingSource VIKINGv20151230 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vikingSource VIKINGv20160406 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vikingSource VIKINGv20161202 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vikingSource VIKINGv20170715 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vikingSource VIKINGv20181012 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vikingSource, vikingSourceRemeasurement VIKINGv20110714 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource VMCDR2 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource VMCDR3 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCDR4 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20110909 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource VMCv20120126 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource VMCv20121128 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource VMCv20130304 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource VMCv20130805 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource VMCv20140428 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20140903 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20150309 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20151218 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20160311 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20160822 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20170109 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20170411 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20171101 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20180702 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource VMCv20181120 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClass vmcSource, vmcSourceRemeasurement VMCv20110816 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vmcSource, vmcSynopticSource VMCDR1 discrete image classification flag in Y smallint 2   -9999 src.class
yClass vvvSource, vvvSynopticSource VVVDR4 discrete image classification flag in Y smallint 2   -9999 src.class;em.IR.NIR
yClassStat vhsSource VHSDR2 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vhsSource VHSDR3 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSDR4 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSv20120926 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vhsSource VHSv20130417 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vhsSource VHSv20140409 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSv20150108 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSv20160114 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSv20160507 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSv20170630 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSv20171207 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource VHSv20180419 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vhsSource, vhsSourceRemeasurement VHSDR1 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat videoSource VIDEODR2 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat
yClassStat videoSource VIDEODR3 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat
yClassStat videoSource VIDEODR4 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat videoSource VIDEODR5 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat videoSource VIDEOv20100513 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat
yClassStat videoSource VIDEOv20111208 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat
yClassStat videoSourceRemeasurement VIDEOv20100513 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingSource VIKINGDR2 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingSource VIKINGDR3 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingSource VIKINGDR4 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vikingSource VIKINGv20111019 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingSource VIKINGv20130417 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingSource VIKINGv20140402 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingSource VIKINGv20150421 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vikingSource VIKINGv20151230 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vikingSource VIKINGv20160406 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vikingSource VIKINGv20161202 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vikingSource VIKINGv20170715 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vikingSource VIKINGv20181012 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vikingSource, vikingSourceRemeasurement VIKINGv20110714 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource VMCDR2 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource VMCDR3 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCDR4 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20110909 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource VMCv20120126 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource VMCv20121128 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource VMCv20130304 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource VMCv20130805 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource VMCv20140428 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20140903 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20150309 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20151218 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20160311 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20160822 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20170109 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20170411 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20171101 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20180702 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource VMCv20181120 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vmcSource, vmcSourceRemeasurement VMCv20110816 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vmcSource, vmcSynopticSource VMCDR1 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat
yClassStat vvvSource VVVDR4 S-Extractor classification statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
yClassStat vvvSynopticSource VVVDR4 N(0,1) stellarness-of-profile statistic in Y real 4   -0.9999995e9 stat;em.IR.NIR
ycStratAst videoVarFrameSetInfo VIDEODR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst videoVarFrameSetInfo VIDEODR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst videoVarFrameSetInfo VIDEODR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst videoVarFrameSetInfo VIDEODR5 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCDR1 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCDR2 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCDR3 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCDR4 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20110816 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20110909 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20120126 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20121128 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20130304 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20130805 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20140428 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20140903 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20150309 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20151218 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20160311 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20160822 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20170109 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20170411 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20171101 Strateva parameter, c, in fit to astrometric rms vs magnitude in Y 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.
ycStratAst vmcVarFrameSetInfo VMCv20180702 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band. 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.
ycStratAst vmcVarFrameSetInfo VMCv20181120 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Y band. 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.
ycStratPht videoVarFrameSetInfo VIDEODR2 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht videoVarFrameSetInfo VIDEODR3 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht videoVarFrameSetInfo VIDEODR4 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht videoVarFrameSetInfo VIDEODR5 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht videoVarFrameSetInfo VIDEOv20100513 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht videoVarFrameSetInfo VIDEOv20111208 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vikingVarFrameSetInfo VIKINGv20110714 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCDR1 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCDR2 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCDR3 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCDR4 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20110816 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20110909 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20120126 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20121128 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20130304 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20130805 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20140428 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20140903 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20150309 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20151218 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20160311 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20160822 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20170109 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20170411 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20171101 Strateva parameter, c, in fit to photometric rms vs magnitude in Y 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.
ycStratPht vmcVarFrameSetInfo VMCv20180702 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band. 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.
ycStratPht vmcVarFrameSetInfo VMCv20181120 Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Y band. 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.
yDeblend vhsSourceRemeasurement VHSDR1 placeholder flag indicating parent/child relation in Y int 4   -99999999 meta.code
yDeblend videoSource, videoSourceRemeasurement VIDEOv20100513 placeholder flag indicating parent/child relation in Y int 4   -99999999 meta.code
yDeblend vikingSourceRemeasurement VIKINGv20110714 placeholder flag indicating parent/child relation in Y int 4   -99999999 meta.code
yDeblend vikingSourceRemeasurement VIKINGv20111019 placeholder flag indicating parent/child relation in Y int 4   -99999999 meta.code
yDeblend vmcSourceRemeasurement VMCv20110816 placeholder flag indicating parent/child relation in Y int 4   -99999999 meta.code
yDeblend vmcSourceRemeasurement VMCv20110909 placeholder flag indicating parent/child relation in Y int 4   -99999999 meta.code
Yell vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR4 Ellipticity of the DR4 Y detection. {catalogue TType keyword: Yell} real 4   -999999500.0  
yEll vhsSource VHSDR2 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vhsSource VHSDR3 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSDR4 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSv20120926 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vhsSource VHSv20130417 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vhsSource VHSv20140409 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSv20150108 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSv20160114 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSv20160507 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSv20170630 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSv20171207 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource VHSv20180419 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vhsSource, vhsSourceRemeasurement VHSDR1 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll videoSource VIDEODR2 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll videoSource VIDEODR3 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll videoSource VIDEODR4 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll videoSource VIDEODR5 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll videoSource VIDEOv20111208 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll videoSource, videoSourceRemeasurement VIDEOv20100513 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingSource VIKINGDR2 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingSource VIKINGDR3 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingSource VIKINGDR4 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vikingSource VIKINGv20111019 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingSource VIKINGv20130417 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingSource VIKINGv20140402 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingSource VIKINGv20150421 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vikingSource VIKINGv20151230 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vikingSource VIKINGv20160406 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vikingSource VIKINGv20161202 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vikingSource VIKINGv20170715 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vikingSource VIKINGv20181012 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vikingSource, vikingSourceRemeasurement VIKINGv20110714 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource VMCDR2 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource VMCDR3 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCDR4 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20110909 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource VMCv20120126 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource VMCv20121128 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource VMCv20130304 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource VMCv20130805 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource VMCv20140428 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20140903 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20150309 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20151218 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20160311 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20160822 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20170109 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20170411 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20171101 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20180702 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource VMCv20181120 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yEll vmcSource, vmcSourceRemeasurement VMCv20110816 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vmcSource, vmcSynopticSource VMCDR1 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity
yEll vvvSource, vvvSynopticSource VVVDR4 1-b/a, where a/b=semi-major/minor axes in Y real 4   -0.9999995e9 src.ellipticity;em.IR.NIR
yeNum vhsMergeLog VHSDR1 the extension number of this Y frame tinyint 1     meta.number
yeNum vhsMergeLog VHSDR2 the extension number of this Y frame tinyint 1     meta.number
yeNum vhsMergeLog VHSDR3 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSDR4 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSv20120926 the extension number of this Y frame tinyint 1     meta.number
yeNum vhsMergeLog VHSv20130417 the extension number of this Y frame tinyint 1     meta.number
yeNum vhsMergeLog VHSv20140409 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSv20150108 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSv20160114 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSv20160507 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSv20170630 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSv20171207 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vhsMergeLog VHSv20180419 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum videoMergeLog VIDEODR2 the extension number of this Y frame tinyint 1     meta.number
yeNum videoMergeLog VIDEODR3 the extension number of this Y frame tinyint 1     meta.number
yeNum videoMergeLog VIDEODR4 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum videoMergeLog VIDEODR5 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum videoMergeLog VIDEOv20100513 the extension number of this Y frame tinyint 1     meta.number
yeNum videoMergeLog VIDEOv20111208 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingMergeLog VIKINGDR2 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingMergeLog VIKINGDR3 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingMergeLog VIKINGDR4 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vikingMergeLog VIKINGv20110714 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingMergeLog VIKINGv20111019 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingMergeLog VIKINGv20130417 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingMergeLog VIKINGv20140402 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingMergeLog VIKINGv20150421 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vikingMergeLog VIKINGv20151230 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vikingMergeLog VIKINGv20160406 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vikingMergeLog VIKINGv20161202 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vikingMergeLog VIKINGv20170715 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vikingMergeLog VIKINGv20181012 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20160909 the extension number of this Y frame tinyint 1     meta.number
yeNum vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20170124 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCDR2 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCDR3 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCDR4 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20110816 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCv20110909 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCv20120126 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCv20121128 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCv20130304 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCv20130805 the extension number of this Y frame tinyint 1     meta.number
yeNum vmcMergeLog VMCv20140428 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20140903 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20150309 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20151218 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20160311 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20160822 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20170109 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20170411 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20171101 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20180702 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog VMCv20181120 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yeNum vmcMergeLog, vmcSynopticMergeLog VMCDR1 the extension number of this Y frame tinyint 1     meta.number
yeNum vvvMergeLog, vvvSynopticMergeLog VVVDR4 the extension number of this Y frame tinyint 1     meta.number;em.IR.NIR
yErr vhsDetection VHSDR2 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSDR3 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSDR4 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20120926 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20130417 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20140409 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20150108 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20160114 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20160507 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20170630 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20171207 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection VHSv20180419 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vhsDetection, vhsListRemeasurement VHSDR1 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEODR2 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEODR3 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEODR4 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEODR5 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEOv20100513 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoDetection VIDEOv20111208 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr videoListRemeasurement VIDEOv20100513 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGDR2 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGDR3 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGDR4 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20111019 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20130417 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20140402 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20150421 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20151230 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20160406 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20161202 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20170715 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection VIKINGv20181012 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingDetection, vikingListRemeasurement VIKINGv20110714 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingMapRemeasurement VIKINGZYSELJv20160909 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vikingMapRemeasurement VIKINGZYSELJv20170124 Error in Y coordinate (SE: ERRY2_IMAGE½) {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCDR1 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCDR2 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCDR3 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCDR4 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20110909 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20120126 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20121128 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20130304 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20130805 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20140428 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20140903 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20150309 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20151218 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20160311 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20160822 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20170109 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20170411 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20171101 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20180702 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection VMCv20181120 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vmcDetection, vmcListRemeasurement VMCv20110816 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
yErr vvvDetection VVVDR4 Error in Y coordinate {catalogue TType keyword: Y_coordinate_err}
Estimate of centroid error.
real 4 pixels   stat.error
YERR_R spectra SIXDF error on Y_R position int 4      
YERR_V spectra SIXDF error on Y_V position int 4      
yErrBits vhsSource VHSDR1 processing warning/error bitwise flags in Y 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.
yErrBits vhsSource VHSDR2 processing warning/error bitwise flags in Y 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.
yErrBits vhsSource VHSDR3 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSDR4 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSv20120926 processing warning/error bitwise flags in Y 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.
yErrBits vhsSource VHSv20130417 processing warning/error bitwise flags in Y 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.
yErrBits vhsSource VHSv20140409 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSv20150108 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSv20160114 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSv20160507 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSv20170630 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSv20171207 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSource VHSv20180419 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vhsSourceRemeasurement VHSDR1 processing warning/error bitwise flags in Y int 4   -99999999 meta.code
yErrBits videoSource VIDEODR2 processing warning/error bitwise flags in Y 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

yErrBits videoSource VIDEODR3 processing warning/error bitwise flags in Y 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

yErrBits videoSource VIDEODR4 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
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

yErrBits videoSource VIDEODR5 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
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

yErrBits videoSource VIDEOv20100513 processing warning/error bitwise flags in Y 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

yErrBits videoSource VIDEOv20111208 processing warning/error bitwise flags in Y 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

yErrBits videoSourceRemeasurement VIDEOv20100513 processing warning/error bitwise flags in Y int 4   -99999999 meta.code
yErrBits vikingSource VIKINGDR2 processing warning/error bitwise flags in Y 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.
yErrBits vikingSource VIKINGDR3 processing warning/error bitwise flags in Y 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.
yErrBits vikingSource VIKINGDR4 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vikingSource VIKINGv20110714 processing warning/error bitwise flags in Y 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.
yErrBits vikingSource VIKINGv20111019 processing warning/error bitwise flags in Y 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.
yErrBits vikingSource VIKINGv20130417 processing warning/error bitwise flags in Y 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.
yErrBits vikingSource VIKINGv20140402 processing warning/error bitwise flags in Y 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.
yErrBits vikingSource VIKINGv20150421 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vikingSource VIKINGv20151230 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vikingSource VIKINGv20160406 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vikingSource VIKINGv20161202 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vikingSource VIKINGv20170715 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vikingSource VIKINGv20181012 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vikingSourceRemeasurement VIKINGv20110714 processing warning/error bitwise flags in Y int 4   -99999999 meta.code
yErrBits vikingSourceRemeasurement VIKINGv20111019 processing warning/error bitwise flags in Y int 4   -99999999 meta.code
yErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 processing warning/error bitwise flags in Y 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.
yErrBits vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCDR2 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCDR3 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCDR4 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20110816 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCv20110909 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCv20120126 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCv20121128 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCv20130304 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCv20130805 processing warning/error bitwise flags in Y 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.
yErrBits vmcSource VMCv20140428 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20140903 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20150309 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20151218 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20160311 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20160822 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20170109 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20170411 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20171101 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20180702 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource VMCv20181120 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yErrBits vmcSource, vmcSynopticSource VMCDR1 processing warning/error bitwise flags in Y 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.
yErrBits vmcSourceRemeasurement VMCv20110816 processing warning/error bitwise flags in Y int 4   -99999999 meta.code
yErrBits vmcSourceRemeasurement VMCv20110909 processing warning/error bitwise flags in Y int 4   -99999999 meta.code
yErrBits vvvSource, vvvSynopticSource VVVDR4 processing warning/error bitwise flags in Y int 4   -99999999 meta.code;em.IR.NIR
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
yEta vhsSource VHSDR1 Offset of Y 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.
yEta vhsSource VHSDR2 Offset of Y 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.
yEta vhsSource VHSDR3 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSDR4 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSv20120926 Offset of Y 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.
yEta vhsSource VHSv20130417 Offset of Y 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.
yEta vhsSource VHSv20140409 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSv20150108 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSv20160114 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSv20160507 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSv20170630 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSv20171207 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vhsSource VHSv20180419 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta videoSource VIDEODR2 Offset of Y 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.
yEta videoSource VIDEODR3 Offset of Y 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.
yEta videoSource VIDEODR4 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta videoSource VIDEODR5 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta videoSource VIDEOv20100513 Offset of Y 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.
yEta videoSource VIDEOv20111208 Offset of Y 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.
yEta vikingSource VIKINGDR2 Offset of Y 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.
yEta vikingSource VIKINGDR3 Offset of Y 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.
yEta vikingSource VIKINGDR4 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vikingSource VIKINGv20110714 Offset of Y 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.
yEta vikingSource VIKINGv20111019 Offset of Y 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.
yEta vikingSource VIKINGv20130417 Offset of Y 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.
yEta vikingSource VIKINGv20140402 Offset of Y 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.
yEta vikingSource VIKINGv20150421 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vikingSource VIKINGv20151230 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vikingSource VIKINGv20160406 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vikingSource VIKINGv20161202 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vikingSource VIKINGv20170715 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vikingSource VIKINGv20181012 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCDR2 Offset of Y 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.
yEta vmcSource VMCDR3 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCDR4 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20110816 Offset of Y 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.
yEta vmcSource VMCv20110909 Offset of Y 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.
yEta vmcSource VMCv20120126 Offset of Y 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.
yEta vmcSource VMCv20121128 Offset of Y 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.
yEta vmcSource VMCv20130304 Offset of Y 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.
yEta vmcSource VMCv20130805 Offset of Y 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.
yEta vmcSource VMCv20140428 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20140903 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20150309 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20151218 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20160311 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20160822 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20170109 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20170411 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20171101 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20180702 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource VMCv20181120 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yEta vmcSource, vmcSynopticSource VMCDR1 Offset of Y 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.
yEta vvvSource, vvvSynopticSource VVVDR4 Offset of Y detection from master position (+north/-south) real 4 arcsec -0.9999995e9 pos.eq.dec;arith.diff;em.IR.NIR
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.
yexpML videoVarFrameSetInfo VIDEODR2 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML videoVarFrameSetInfo VIDEODR3 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9 phot.mag;stat.max;em.IR.NIR
yexpML videoVarFrameSetInfo VIDEODR4 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML videoVarFrameSetInfo VIDEODR5 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML videoVarFrameSetInfo VIDEOv20100513 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML videoVarFrameSetInfo VIDEOv20111208 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML vikingVarFrameSetInfo VIKINGv20110714 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML vmcVarFrameSetInfo VMCDR1 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML vmcVarFrameSetInfo VMCDR2 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max;em.IR.NIR
yexpML vmcVarFrameSetInfo VMCDR3 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCDR4 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20110816 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML vmcVarFrameSetInfo VMCv20110909 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML vmcVarFrameSetInfo VMCv20120126 Expected magnitude limit of frameSet in this in Y band. real 4   -0.9999995e9  
yexpML vmcVarFrameSetInfo VMCv20121128 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
yexpML vmcVarFrameSetInfo VMCv20130304 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;stat.max;em.IR.NIR
yexpML vmcVarFrameSetInfo VMCv20130805 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max;em.IR.NIR
yexpML vmcVarFrameSetInfo VMCv20140428 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20140903 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20150309 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20151218 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20160311 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20160822 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20170109 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20170411 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20171101 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20180702 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yexpML vmcVarFrameSetInfo VMCv20181120 Expected magnitude limit of frameSet in this in Y band. real 4 mag -0.9999995e9 phot.mag;em.IR.NIR;stat.max
yExpRms videoVariability VIDEODR2 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms videoVariability VIDEODR3 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms videoVariability VIDEODR4 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms videoVariability VIDEODR5 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms videoVariability VIDEOv20100513 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms videoVariability VIDEOv20111208 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vikingVariability VIKINGv20110714 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCDR1 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCDR2 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCDR3 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCDR4 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20110816 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20110909 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20120126 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20121128 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20130304 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20130805 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20140428 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20140903 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20150309 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20151218 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20160311 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20160822 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20170109 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20170411 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20171101 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20180702 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yExpRms vmcVariability VMCv20181120 Rms calculated from polynomial fit to modal RMS as a function of magnitude in Y 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.
yGausig vhsSource VHSDR2 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vhsSource VHSDR3 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSDR4 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSv20120926 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vhsSource VHSv20130417 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vhsSource VHSv20140409 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSv20150108 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSv20160114 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSv20160507 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSv20170630 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSv20171207 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource VHSv20180419 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vhsSource, vhsSourceRemeasurement VHSDR1 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig videoSource VIDEODR2 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig videoSource VIDEODR3 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig videoSource VIDEODR4 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig videoSource VIDEODR5 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig videoSource VIDEOv20111208 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig videoSource, videoSourceRemeasurement VIDEOv20100513 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingSource VIKINGDR2 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingSource VIKINGDR3 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingSource VIKINGDR4 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vikingSource VIKINGv20111019 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingSource VIKINGv20130417 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingSource VIKINGv20140402 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingSource VIKINGv20150421 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vikingSource VIKINGv20151230 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vikingSource VIKINGv20160406 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vikingSource VIKINGv20161202 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vikingSource VIKINGv20170715 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vikingSource VIKINGv20181012 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vikingSource, vikingSourceRemeasurement VIKINGv20110714 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource VMCDR2 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource VMCDR3 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCDR4 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20110909 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource VMCv20120126 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource VMCv20121128 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource VMCv20130304 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource VMCv20130805 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource VMCv20140428 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20140903 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20150309 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20151218 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20160311 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20160822 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20170109 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20170411 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20171101 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20180702 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource VMCv20181120 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yGausig vmcSource, vmcSourceRemeasurement VMCv20110816 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vmcSource, vmcSynopticSource VMCDR1 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param
yGausig vvvSource, vvvSynopticSource VVVDR4 RMS of axes of ellipse fit in Y real 4 pixels -0.9999995e9 src.morph.param;em.IR.NIR
yHalfRad videoSource VIDEODR4 SExtractor half-light radius in Y band real 4 pixels -0.9999995e9 phys.angSize;em.IR.NIR
yHalfRad videoSource VIDEODR5 SExtractor half-light radius in Y band real 4 pixels -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSDR1 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs vhsSource VHSDR2 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs vhsSource VHSDR3 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSDR4 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSv20120926 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize
yHlCorSMjRadAs vhsSource VHSv20130417 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize
yHlCorSMjRadAs vhsSource VHSv20140409 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSv20150108 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSv20160114 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSv20160507 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSv20170630 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSv20171207 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vhsSource VHSv20180419 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs videoSource VIDEODR2 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs videoSource VIDEODR3 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize
yHlCorSMjRadAs videoSource VIDEODR4 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs videoSource VIDEODR5 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs videoSource VIDEOv20100513 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs videoSource VIDEOv20111208 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs vikingSource VIKINGDR2 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs vikingSource VIKINGDR3 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize
yHlCorSMjRadAs vikingSource VIKINGDR4 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vikingSource VIKINGv20110714 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs vikingSource VIKINGv20111019 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;src
yHlCorSMjRadAs vikingSource VIKINGv20130417 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize
yHlCorSMjRadAs vikingSource VIKINGv20140402 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize
yHlCorSMjRadAs vikingSource VIKINGv20150421 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vikingSource VIKINGv20151230 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vikingSource VIKINGv20160406 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vikingSource VIKINGv20161202 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vikingSource VIKINGv20170715 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yHlCorSMjRadAs vikingSource VIKINGv20181012 Seeing corrected half-light, semi-major axis in Y band real 4 arcsec -0.9999995e9 phys.angSize;em.IR.NIR
yIntRms videoVariability VIDEODR2 Intrinsic rms in Y-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.
yIntRms videoVariability VIDEODR3 Intrinsic rms in Y-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.
yIntRms videoVariability VIDEODR4 Intrinsic rms in Y-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.
yIntRms videoVariability VIDEODR5 Intrinsic rms in Y-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.
yIntRms videoVariability VIDEOv20100513 Intrinsic rms in Y-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.
yIntRms videoVariability VIDEOv20111208 Intrinsic rms in Y-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.
yIntRms vikingVariability VIKINGv20110714 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCDR1 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCDR2 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCDR3 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCDR4 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20110816 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20110909 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20120126 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20121128 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20130304 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20130805 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20140428 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20140903 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20150309 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20151218 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20160311 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20160822 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20170109 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20170411 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20171101 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20180702 Intrinsic rms in Y-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.
yIntRms vmcVariability VMCv20181120 Intrinsic rms in Y-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.
yisDefAst videoVarFrameSetInfo VIDEODR2 Use a default model for the astrometric noise in Y band. tinyint 1   0  
yisDefAst videoVarFrameSetInfo VIDEODR3 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst videoVarFrameSetInfo VIDEODR4 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst videoVarFrameSetInfo VIDEODR5 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst videoVarFrameSetInfo VIDEOv20111208 Use a default model for the astrometric noise in Y band. tinyint 1   0  
yisDefAst vmcVarFrameSetInfo VMCDR1 Use a default model for the astrometric noise in Y band. tinyint 1   0  
yisDefAst vmcVarFrameSetInfo VMCDR2 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCDR3 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCDR4 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20110816 Use a default model for the astrometric noise in Y band. tinyint 1   0  
yisDefAst vmcVarFrameSetInfo VMCv20110909 Use a default model for the astrometric noise in Y band. tinyint 1   0  
yisDefAst vmcVarFrameSetInfo VMCv20120126 Use a default model for the astrometric noise in Y band. tinyint 1   0  
yisDefAst vmcVarFrameSetInfo VMCv20121128 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20130304 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20130805 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20140428 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20140903 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20150309 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20151218 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20160311 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20160822 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20170109 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20170411 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20171101 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20180702 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefAst vmcVarFrameSetInfo VMCv20181120 Use a default model for the astrometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht videoVarFrameSetInfo VIDEODR2 Use a default model for the photometric noise in Y band. tinyint 1   0  
yisDefPht videoVarFrameSetInfo VIDEODR3 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht videoVarFrameSetInfo VIDEODR4 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht videoVarFrameSetInfo VIDEODR5 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht videoVarFrameSetInfo VIDEOv20111208 Use a default model for the photometric noise in Y band. tinyint 1   0  
yisDefPht vmcVarFrameSetInfo VMCDR1 Use a default model for the photometric noise in Y band. tinyint 1   0  
yisDefPht vmcVarFrameSetInfo VMCDR2 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCDR3 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCDR4 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20110816 Use a default model for the photometric noise in Y band. tinyint 1   0  
yisDefPht vmcVarFrameSetInfo VMCv20110909 Use a default model for the photometric noise in Y band. tinyint 1   0  
yisDefPht vmcVarFrameSetInfo VMCv20120126 Use a default model for the photometric noise in Y band. tinyint 1   0  
yisDefPht vmcVarFrameSetInfo VMCv20121128 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20130304 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20130805 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20140428 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20140903 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20150309 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20151218 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20160311 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20160822 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20170109 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20170411 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20171101 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20180702 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yisDefPht vmcVarFrameSetInfo VMCv20181120 Use a default model for the photometric noise in Y band. tinyint 1   0 meta.code;em.IR.NIR
yIsMeas vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Is pass band Y measured? 0 no, 1 yes tinyint 1   0 meta.code
yIsMeas vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Is pass band Y measured? 0 no, 1 yes tinyint 1   0 meta.code
yjiWS vmcVariability VMCDR1 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCDR2 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCDR3 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCDR4 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20110816 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20110909 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20120126 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20121128 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20130304 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20130805 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20140428 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20140903 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20150309 Welch-Stetson statistic between Y and J. 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.
yjiWS vmcVariability VMCv20151218 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20160311 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20160822 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20170109 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20170411 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20171101 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20180702 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yjiWS vmcVariability VMCv20181120 Welch-Stetson statistic between Y and J. 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.NIR;em.IR.J
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.
yKronMag videoSource VIDEODR4 Extended source Y mag (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yKronMag videoSource VIDEODR5 Extended source Y mag (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 phot.mag;em.IR.NIR
yKronMagErr videoSource VIDEODR4 Extended source Y mag error (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
yKronMagErr videoSource VIDEODR5 Extended source Y mag error (Kron - SExtractor MAG_AUTO) real 4 mag -0.9999995e9 stat.error;em.IR.NIR;phot.mag
Ymag vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR4 VVV DR4 Y photometry {catalogue TType keyword: Ymag} real 4 mag -999999500.0  
yMag vhsSourceRemeasurement VHSDR1 Y mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
yMag videoSourceRemeasurement VIDEOv20100513 Y mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
yMag vikingSourceRemeasurement VIKINGv20110714 Y mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
yMag vikingSourceRemeasurement VIKINGv20111019 Y mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
yMag vmcSourceRemeasurement VMCv20110816 Y mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
yMag vmcSourceRemeasurement VMCv20110909 Y mag (as appropriate for this merged source) real 4 mag -0.9999995e9 phot.mag
yMagErr vhsSourceRemeasurement VHSDR1 Error in Y mag real 4 mag -0.9999995e9 stat.error
yMagErr videoSourceRemeasurement VIDEOv20100513 Error in Y mag real 4 mag -0.9999995e9 stat.error
yMagErr vikingSourceRemeasurement VIKINGv20110714 Error in Y mag real 4 mag -0.9999995e9 stat.error
yMagErr vikingSourceRemeasurement VIKINGv20111019 Error in Y mag real 4 mag -0.9999995e9 stat.error
yMagErr vmcCepheidVariables VMCDR4 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcCepheidVariables VMCv20160311 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcCepheidVariables VMCv20160822 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcCepheidVariables VMCv20170109 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcCepheidVariables VMCv20170411 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcCepheidVariables VMCv20171101 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcCepheidVariables VMCv20180702 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcCepheidVariables VMCv20181120 Error in intensity-averaged Y band magnitude {catalogue TType keyword: e_Ymag} real 4 mag -0.9999995e9 stat.error;phot.mag;em.IR.NIR
yMagErr vmcSourceRemeasurement VMCv20110816 Error in Y mag real 4 mag -0.9999995e9 stat.error
yMagErr vmcSourceRemeasurement VMCv20110909 Error in Y mag real 4 mag -0.9999995e9 stat.error
yMagMAD videoVariability VIDEODR2 Median Absolute Deviation of Y 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.
yMagMAD videoVariability VIDEODR3 Median Absolute Deviation of Y 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.
yMagMAD videoVariability VIDEODR4 Median Absolute Deviation of Y magnitude real 4 mag -0.9999995e9 stat.err;em.IR.NIR;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.
yMagMAD videoVariability VIDEODR5 Median Absolute Deviation of Y magnitude real 4 mag -0.9999995e9 stat.err;em.IR.NIR;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.
yMagMAD videoVariability VIDEOv20100513 Median Absolute Deviation of Y 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.
yMagMAD videoVariability VIDEOv20111208 Median Absolute Deviation of Y 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.
yMagMAD vikingVariability VIKINGv20110714 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCDR1 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCDR2 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCDR3 Median Absolute Deviation of Y magnitude real 4 mag -0.9999995e9 stat.err;em.IR.NIR;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.
yMagMAD vmcVariability VMCDR4 Median Absolute Deviation of Y magnitude real 4 mag -0.9999995e9 stat.err;em.IR.NIR;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.
yMagMAD vmcVariability VMCv20110816 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCv20110909 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCv20120126 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCv20121128 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCv20130304 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCv20130805 Median Absolute Deviation of Y 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.
yMagMAD vmcVariability VMCv20140428 Median Absolute Deviation of Y magnitude real 4 mag -0.9999995e9 stat.err;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.
yMagMAD vmcVariability VMCv20140903 Median Absolute Deviation of Y magnitude real 4 mag -0.9999995e9 stat.err;em.IR.NIR;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.
yMagMAD vmcVariability VMCv20150309 Median Absolute Deviation of Y magnitude real 4 mag -0.9999995e9 stat.err;em.IR.NIR;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.