VSA Browser

Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20230922 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20230922 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

P

Name Schema Table Database Description Type Length Unit Default Value Unified Content Descriptor

p1 catwise_2020, catwise_prelim WISE P vector component 1 real 4 arcsec

p1 cepheid, rrlyrae GAIADR1 Period corresponding to the maximum peak in the periodogram of G band time series float 8 days time.period

p1_error cepheid, rrlyrae GAIADR1 Uncertainty on the period corresponding to the maximum peak in the periodogram of G band time series float 8 days stat.error;time.period

p2 catwise_2020, catwise_prelim WISE P vector component 2 real 4 arcsec

PA combo17CDFSSource COMBO17 position angle, measured West to North real 4 deg

PA nvssSource NVSS [-90, 90] Position angle of fitted major axis real 4 degress pos.posAng

pa first08Jul16Source, firstSource, firstSource12Feb16 FIRST position angle (east of north) derived from the elliptical Gaussian model for the source real 4 degrees pos.posAng

pa sharksDetection SHARKSv20210222 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa sharksDetection SHARKSv20210421 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa ultravistaDetection, ultravistaMapRemeasurement ULTRAVISTADR4 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa ultravistaMapRemeasAver ULTRAVISTADR4 Averaged ellipse fit orientation to x axis
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa vhsDetection VHSDR2 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSDR3 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSDR4 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSDR5 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSDR6 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20120926 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20130417 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20140409 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20150108 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20160114 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20160507 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20170630 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20180419 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection VHSv20201209 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vhsDetection, vhsListRemeasurement VHSDR1 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa videoDetection VIDEODR2 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa videoDetection VIDEODR3 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa videoDetection VIDEODR4 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa videoDetection VIDEODR5 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa videoDetection VIDEOv20100513 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa videoDetection VIDEOv20111208 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa videoListRemeasurement VIDEOv20100513 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGDR2 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGDR3 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGDR4 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20111019 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20130417 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20140402 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20150421 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20151230 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20160406 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20161202 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection VIKINGv20170715 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingDetection, vikingListRemeasurement VIKINGv20110714 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vikingMapRemeasAver VIKINGZYSELJv20160909 Averaged ellipse fit orientation to x axis
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa vikingMapRemeasAver VIKINGZYSELJv20170124 Averaged ellipse fit orientation to x axis
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa vikingMapRemeasurement VIKINGZYSELJv20160909 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa vikingMapRemeasurement VIKINGZYSELJv20170124 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise. real 4 degrees pos.posAng

pa vmcDetection VMCDR1 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCDR2 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCDR3 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCDR4 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCDR5 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20110909 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20120126 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20121128 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20130304 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20130805 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20140428 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20140903 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20150309 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20151218 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20160311 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20160822 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20170109 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20170411 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20171101 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20180702 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20181120 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20191212 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20210708 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection VMCv20230816 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcDetection, vmcListRemeasurement VMCv20110816 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vmcdeepDetection VMCDEEPv20230713 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vvvDetection VVVDR1 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vvvDetection VVVDR2 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vvvDetection, vvvListRemeasurement VVVv20100531 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa_2mass allwise_sc WISE Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source. float 8 deg

pa_2mass wise_allskysc WISE Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source, default if there is no associated 2MASS PSC source. real 4 degrees -0.9999995e9

pa_2mass wise_prelimsc WISE Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source, default if there is no associated 2MASS PSC source real 4 degrees -0.9999995e9

pairingCriterion Programme SHARKSv20210222 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme SHARKSv20210421 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ULTRAVISTADR4 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSDR1 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSDR2 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSDR3 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSDR4 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSDR5 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSDR6 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20120926 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20130417 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20150108 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20160114 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20160507 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20170630 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20180419 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VHSv20201209 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIDEODR2 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIDEODR3 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIDEODR4 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIDEODR5 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIDEOv20100513 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIDEOv20111208 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGDR2 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGDR3 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGDR4 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20110714 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20111019 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20130417 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20150421 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20151230 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20160406 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20161202 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VIKINGv20170715 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCDEEPv20230713 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCDR1 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCDR3 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCDR4 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCDR5 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20110816 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20110909 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20120126 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20121128 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20130304 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20130805 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20140428 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20140903 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20150309 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20151218 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20160311 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20160822 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20170109 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20170411 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20171101 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20180702 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20181120 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20191212 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20210708 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VMCv20230816 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VSAQC The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VVVDR1 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VVVDR2 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VVVDR5 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VVVv20100531 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VVVv20110718 The pairing criterion for associating detections into merged sources real 4 Degrees ??

par_pm catwise_2020, catwise_prelim WISE parallax from PM desc-asce elon real 4 arcsec

par_pm is computed from the motion-solution positions, which are translated by WPHotpmc to the standard epoch (MJD0), so except for estimation errors, par_pm is the parallax; par_pm will be null unless km = 3.

par_pmSig catwise_2020, catwise_prelim WISE one-sigma uncertainty in par_pm real 4 arcsec

par_sigma catwise_2020, catwise_prelim WISE one-sigma uncertainty in par_stat real 4 arcsec

par_stat catwise_2020, catwise_prelim WISE parallax estimate from stationary solution real 4 arcsec

The par_stat column is computed by using the motion estimate to move the ascending stationary-solution position from the ascending effective observation epoch to that of the descending solution, then dividing the ecliptic longitude difference by 2; par_stat will be null unless ka = 3 AND km > 0 AND all W?mJDmin/max/mean values are non-null in both ascending and descending mdex files.

parallax gaia_source GAIADR2 Parallax float 8 milliarcsec pos.parallax

parallax gaia_source GAIAEDR3 Parallax float 8 milliarcsec pos.parallax

parallax gaia_source, tgas_source GAIADR1 Parallax float 8 milliarcsec pos.parallax

parallax ravedr5Source RAVE spectrophotometric Parallax (Binney et al. 2014) real 4 mas pos.parallax

parallax sharksVariability SHARKSv20210222 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax sharksVariability SHARKSv20210421 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax ultravistaVariability ULTRAVISTADR4 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax videoVariability VIDEODR2 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax videoVariability VIDEODR3 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax videoVariability VIDEODR4 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax videoVariability VIDEODR5 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax videoVariability VIDEOv20100513 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax videoVariability VIDEOv20111208 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGDR2 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGDR3 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGDR4 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20110714 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20111019 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20130417 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20140402 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20150421 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20151230 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20160406 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20161202 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vikingVariability VIKINGv20170715 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCDR1 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCDR2 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCDR3 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCDR4 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCDR5 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20110816 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20110909 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20120126 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20121128 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20130304 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20130805 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20140428 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20140903 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20150309 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20151218 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20160311 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20160822 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20170109 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20170411 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20171101 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20180702 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20181120 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20191212 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20210708 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcVariability VMCv20230816 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vmcdeepVariability VMCDEEPv20230713 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vvvVariability VVVDR1 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vvvVariability VVVDR2 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vvvVariability VVVDR5 Parallax of star real 4 mas -0.9999995e9 pos.parallax

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vvvVariability VVVv20100531 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax vvvVariability VVVv20110718 Parallax of star real 4 mas -0.9999995e9

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.

parallax_error gaia_source GAIADR2 Standard error of parallax float 8 milliarcsec stat.error;pos.parallax

parallax_error gaia_source GAIAEDR3 Standard error of parallax float 8 milliarcsec stat.error;pos.parallax

parallax_error gaia_source, tgas_source GAIADR1 Standard error of parallax float 8 milliarcsec stat.error;pos.parallax

parallax_error_TGAS ravedr5Source RAVE Error of parallax float 8 mas stat.error;pos.parallax

parallax_over_error gaia_source GAIADR2 Parallax divided by standard error real 4 arith.ratio

parallax_over_error gaia_source GAIAEDR3 Parallax divided by standard error real 4 arith.ratio

parallax_pmdec_corr gaia_source GAIADR2 Correlation between parallax and proper motion in Declination real 4 stat.correlation;pos.parallax;pos.pm;pos.eq.dec

parallax_pmdec_corr gaia_source GAIAEDR3 Correlation between parallax and proper motion in Declination real 4 stat.correlation;pos.parallax;pos.pm;pos.eq.dec

parallax_pmdec_corr gaia_source, tgas_source GAIADR1 Correlation between parallax and proper motion in Declination real 4 stat.correlation

parallax_pmra_corr gaia_source GAIADR2 Correlation between parallax and proper motion in Right Ascension real 4 stat.correlation;pos.parallax;pos.pm;pos.eq.ra

parallax_pmra_corr gaia_source GAIAEDR3 Correlation between parallax and proper motion in Right Ascension real 4 stat.correlation;pos.parallax;pos.pm;pos.eq.ra

parallax_pmra_corr gaia_source, tgas_source GAIADR1 Correlation between parallax and proper motion in Right Ascension real 4 stat.correlation

parallax_pseudocolour_corr gaia_source GAIAEDR3 Correlation between parallax and pseudocolour real 4 stat.correlation;em.wavenumber;pos.parallax

parallax_TGAS ravedr5Source RAVE Parallax float 8 mas pos.parallax

paramTemplate RequiredMosaicTopLevel SHARKSv20210222 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel SHARKSv20210421 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel ULTRAVISTADR4 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VHSv20201209 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VMCDEEPv20230713 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VMCDR5 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VMCv20191212 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VMCv20210708 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VMCv20230816 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VVVDR5 Template file for SWARP parameters varchar 32

PARK grs_ngpSource, grs_ranSource, grs_sgpSource TWODFGRS k classification parameter = k / k_star real 4

PARMU grs_ngpSource, grs_ranSource, grs_sgpSource TWODFGRS mu classification parameter = mu / mu_star real 4

patternString Multiframe SHARKSv20210222 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe SHARKSv20210421 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe ULTRAVISTADR4 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSDR1 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSDR2 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSDR3 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSDR4 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSDR5 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSDR6 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20120926 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20130417 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20140409 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20150108 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20160114 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20160507 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20170630 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20180419 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VHSv20201209 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIDEODR2 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIDEODR3 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIDEODR4 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIDEODR5 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIDEOv20111208 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGDR2 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGDR3 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGDR4 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20110714 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20111019 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20130417 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20140402 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20150421 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20151230 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20160406 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20161202 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VIKINGv20170715 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCDEEPv20230713 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCDR1 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCDR2 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCDR3 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCDR4 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCDR5 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20110816 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20110909 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20120126 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20121128 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20130304 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20130805 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20140428 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20140903 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20150309 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20151218 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20160311 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20160822 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20170109 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20170411 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20171101 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20180702 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20181120 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20191212 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20210708 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VMCv20230816 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VVVDR1 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VVVDR2 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VVVDR5 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString Multiframe VVVv20110718 SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} varchar 64 NONE

patternString sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC SADT pattern ID varchar 64 NONE

pawprintdets vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR5 the number of separate pawprint sets in which a source was detected. Technically 'dets' can be greater than this value where e.g. a high proper motion or faint source is not matched between consecutive observing seasons. {catalogue TType keyword: pawprintdets} int 4 -99999999

peak_to_peak_g cepheid, rrlyrae GAIADR1 Peak-to-peak amplitude of the G band light curve float 8 mag src.var.amplitude;em.opt

peak_to_peak_g_error cepheid, rrlyrae GAIADR1 Uncertainty on peak-to-peak amplitude of the G band light curve float 8 mag stat.error;src.var.amplitude;em.opt

perErr ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource OGLE Uncertainty of period float 8 days stat.error;time.duration

period ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource OGLE Period float 8 days time.period

period vmcCepheidVariables VMCDR3 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCDR4 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20121128 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20140428 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20140903 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20150309 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20151218 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20160311 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20160822 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20170109 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20170411 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20171101 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20180702 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20181120 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20191212 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20210708 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcCepheidVariables VMCv20230816 Period of first mode of oscillation {catalogue TType keyword: Period} real 4 day -0.9999995e9 time.period

period vmcEclipsingBinaryVariables VMCDR4 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20140903 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20150309 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20151218 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20160311 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20160822 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20170109 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20170411 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20171101 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20180702 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20181120 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20191212 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20210708 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcEclipsingBinaryVariables VMCv20230816 Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCDR4 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20160822 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20170109 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20170411 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20171101 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20180702 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20181120 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20191212 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20210708 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period vmcRRlyraeVariables VMCv20230816 Period from OGLE-3 survey {catalogue TType keyword: PERIOD} real 4 day time.period

period1 ogle3LpvLmcSource, ogle3LpvSmcSource OGLE Primary period float 8 days time.period

period2 ogle3LpvLmcSource, ogle3LpvSmcSource OGLE Secondary period (detected automatically) float 8 days time.period

period3 ogle3LpvLmcSource, ogle3LpvSmcSource OGLE Tertiary period (detected automatically) float 8 days time.period

petroFlux sharksDetection SHARKSv20210222 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux sharksDetection SHARKSv20210421 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux ultravistaDetection ULTRAVISTADR4 flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux ultravistaMapRemeasurement ULTRAVISTADR4 flux within Petrosian radius circular aperture (SE: FLUX_PETRO; CASU: default) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSDR2 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vhsDetection VHSDR3 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSDR4 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSDR5 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSDR6 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20120926 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20130417 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20140409 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20150108 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20160114 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20160507 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20170630 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20180419 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection VHSv20201209 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vhsDetection, vhsListRemeasurement VHSDR1 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux videoDetection VIDEODR2 flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux videoDetection VIDEODR3 flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux videoDetection VIDEODR4 flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux videoDetection VIDEODR5 flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux videoDetection VIDEOv20100513 flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux videoDetection VIDEOv20111208 flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux videoListRemeasurement VIDEOv20100513 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vikingDetection VIKINGDR2 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vikingDetection VIKINGDR3 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGDR4 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGv20111019 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vikingDetection VIKINGv20130417 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGv20140402 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGv20150421 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGv20151230 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGv20160406 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGv20161202 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection VIKINGv20170715 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vikingDetection, vikingListRemeasurement VIKINGv20110714 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vikingMapRemeasurement VIKINGZYSELJv20160909 flux within Petrosian radius circular aperture (SE: FLUX_PETRO; CASU: default) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vikingMapRemeasurement VIKINGZYSELJv20170124 flux within Petrosian radius circular aperture (SE: FLUX_PETRO; CASU: default) {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vmcDetection VMCDR1 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vmcDetection VMCDR2 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCDR3 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCDR4 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCDR5 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20110909 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vmcDetection VMCv20120126 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vmcDetection VMCv20121128 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20130304 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20130805 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20140428 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20140903 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20150309 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20151218 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20160311 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20160822 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20170109 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20170411 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20171101 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20180702 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20181120 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20191212 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20210708 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection VMCv20230816 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vmcDetection, vmcListRemeasurement VMCv20110816 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vmcdeepDetection VMCDEEPv20230713 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vvvDetection VVVDR1 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vvvDetection VVVDR2 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count

petroFlux vvvDetection, vvvListRemeasurement VVVv20100531 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFluxErr sharksDetection SHARKSv20210222 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr sharksDetection SHARKSv20210421 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr ultravistaDetection ULTRAVISTADR4 error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr ultravistaMapRemeasurement ULTRAVISTADR4 error on Petrosian flux (SE: FLUXERR_PETRO; CASU: default) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSDR2 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSDR3 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSDR4 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSDR5 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSDR6 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20120926 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20130417 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20140409 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20150108 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20160114 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20160507 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20170630 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20180419 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection VHSv20201209 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vhsDetection, vhsListRemeasurement VHSDR1 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr videoDetection VIDEODR2 error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr videoDetection VIDEODR3 error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr videoDetection VIDEODR4 error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr videoDetection VIDEODR5 error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr videoDetection VIDEOv20100513 error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr videoDetection VIDEOv20111208 error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr videoListRemeasurement VIDEOv20100513 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGDR2 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGDR3 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGDR4 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20111019 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20130417 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20140402 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20150421 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20151230 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20160406 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20161202 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection VIKINGv20170715 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingDetection, vikingListRemeasurement VIKINGv20110714 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingMapRemeasurement VIKINGZYSELJv20160909 error on Petrosian flux (SE: FLUXERR_PETRO; CASU: default) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vikingMapRemeasurement VIKINGZYSELJv20170124 error on Petrosian flux (SE: FLUXERR_PETRO; CASU: default) {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCDR1 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCDR2 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCDR3 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCDR4 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCDR5 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20110909 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20120126 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20121128 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20130304 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20130805 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20140428 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20140903 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20150309 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20151218 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20160311 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20160822 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20170109 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20170411 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20171101 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20180702 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20181120 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20191212 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20210708 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection VMCv20230816 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcDetection, vmcListRemeasurement VMCv20110816 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vmcdeepDetection VMCDEEPv20230713 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vvvDetection VVVDR1 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vvvDetection VVVDR2 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vvvDetection, vvvListRemeasurement VVVv20100531 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroJky ultravistaMapRemeasurement ULTRAVISTADR4 Calibrated Petrosian flux within aperture r_p (CASU: default) real 4 jansky phot.mag

petroJky vikingMapRemeasurement VIKINGZYSELJv20160909 Calibrated Petrosian flux within aperture r_p (CASU: default) real 4 jansky phot.mag

petroJky vikingMapRemeasurement VIKINGZYSELJv20170124 Calibrated Petrosian flux within aperture r_p (CASU: default) real 4 jansky phot.mag

petroJkyErr ultravistaMapRemeasurement ULTRAVISTADR4 error on calibrated Petrosian flux (CASU: default) real 4 jansky stat.error

petroJkyErr vikingMapRemeasurement VIKINGZYSELJv20160909 error on calibrated Petrosian flux (CASU: default) real 4 jansky stat.error

petroJkyErr vikingMapRemeasurement VIKINGZYSELJv20170124 error on calibrated Petrosian flux (CASU: default) real 4 jansky stat.error

petroLup ultravistaMapRemeasurement ULTRAVISTADR4 Calibrated Petrosian luptitude within aperture r_p (CASU: default) real 4 lup phot.mag

petroLup vikingMapRemeasurement VIKINGZYSELJv20160909 Calibrated Petrosian luptitude within aperture r_p (CASU: default) real 4 lup phot.mag

petroLup vikingMapRemeasurement VIKINGZYSELJv20170124 Calibrated Petrosian luptitude within aperture r_p (CASU: default) real 4 lup phot.mag

petroLupErr ultravistaMapRemeasurement ULTRAVISTADR4 error on calibrated Petrosian luptitude (CASU: default) real 4 lup stat.error

petroLupErr vikingMapRemeasurement VIKINGZYSELJv20160909 error on calibrated Petrosian luptitude (CASU: default) real 4 lup stat.error

petroLupErr vikingMapRemeasurement VIKINGZYSELJv20170124 error on calibrated Petrosian luptitude (CASU: default) real 4 lup stat.error

petroMag sharksDetection SHARKSv20210222 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag sharksDetection SHARKSv20210421 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag ultravistaDetection ULTRAVISTADR4 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag ultravistaMapRemeasurement ULTRAVISTADR4 Calibrated Petrosian magnitude within aperture r_p (CASU: default) real 4 mag phot.mag

petroMag vhsDetection VHSDR2 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSDR3 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSDR4 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSDR5 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSDR6 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20120926 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20130417 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20140409 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20150108 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20160114 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20160507 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20170630 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20180419 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection VHSv20201209 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vhsDetection, vhsListRemeasurement VHSDR1 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag videoDetection VIDEODR2 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag videoDetection VIDEODR3 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag videoDetection VIDEODR4 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag videoDetection VIDEODR5 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag videoDetection VIDEOv20111208 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag videoDetection, videoListRemeasurement VIDEOv20100513 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGDR2 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGDR3 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGDR4 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20111019 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20130417 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20140402 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20150421 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20151230 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20160406 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20161202 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection VIKINGv20170715 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingDetection, vikingListRemeasurement VIKINGv20110714 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vikingMapRemeasurement VIKINGZYSELJv20160909 Calibrated Petrosian magnitude within aperture r_p (CASU: default) real 4 mag phot.mag

petroMag vikingMapRemeasurement VIKINGZYSELJv20170124 Calibrated Petrosian magnitude within aperture r_p (CASU: default) real 4 mag phot.mag

petroMag vmcDetection VMCDR1 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCDR2 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCDR3 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCDR4 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCDR5 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20110909 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20120126 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20121128 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20130304 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20130805 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20140428 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20140903 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20150309 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20151218 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20160311 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20160822 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20170109 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20170411 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20171101 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20180702 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20181120 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20191212 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20210708 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection VMCv20230816 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcDetection, vmcListRemeasurement VMCv20110816 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vmcdeepDetection VMCDEEPv20230713 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vvvDetection VVVDR1 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vvvDetection VVVDR2 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vvvDetection, vvvListRemeasurement VVVv20100531 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMagErr sharksDetection SHARKSv20210222 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr sharksDetection SHARKSv20210421 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr ultravistaDetection ULTRAVISTADR4 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr ultravistaMapRemeasurement ULTRAVISTADR4 error on calibrated Petrosian magnitude (CASU: default) real 4 mag stat.error

petroMagErr vhsDetection VHSDR2 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vhsDetection VHSDR3 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vhsDetection VHSDR4 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSDR5 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSDR6 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSv20120926 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vhsDetection VHSv20130417 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vhsDetection VHSv20140409 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vhsDetection VHSv20150108 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSv20160114 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSv20160507 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSv20170630 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSv20180419 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection VHSv20201209 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vhsDetection, vhsListRemeasurement VHSDR1 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr videoDetection VIDEODR2 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr videoDetection VIDEODR3 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr videoDetection VIDEODR4 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr videoDetection VIDEODR5 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr videoDetection VIDEOv20111208 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr videoDetection, videoListRemeasurement VIDEOv20100513 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingDetection VIKINGDR2 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingDetection VIKINGDR3 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingDetection VIKINGDR4 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingDetection VIKINGv20111019 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingDetection VIKINGv20130417 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingDetection VIKINGv20140402 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingDetection VIKINGv20150421 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vikingDetection VIKINGv20151230 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vikingDetection VIKINGv20160406 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vikingDetection VIKINGv20161202 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vikingDetection VIKINGv20170715 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vikingDetection, vikingListRemeasurement VIKINGv20110714 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vikingMapRemeasurement VIKINGZYSELJv20160909 error on calibrated Petrosian magnitude (CASU: default) real 4 mag stat.error

petroMagErr vikingMapRemeasurement VIKINGZYSELJv20170124 error on calibrated Petrosian magnitude (CASU: default) real 4 mag stat.error

petroMagErr vmcDetection VMCDR1 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCDR2 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCDR3 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCDR4 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCDR5 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20110909 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCv20120126 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCv20121128 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCv20130304 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCv20130805 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCv20140428 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcDetection VMCv20140903 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20150309 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20151218 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20160311 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20160822 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20170109 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20170411 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20171101 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20180702 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20181120 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20191212 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20210708 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection VMCv20230816 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vmcDetection, vmcListRemeasurement VMCv20110816 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vmcdeepDetection VMCDEEPv20230713 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vvvDetection VVVDR1 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vvvDetection VVVDR2 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 error on calibrated Petrosian magnitude real 4 mag stat.error;phot.mag

petroMagErr vvvDetection, vvvListRemeasurement VVVv20100531 error on calibrated Petrosian magnitude real 4 mag stat.error

petroRad sharksDetection SHARKSv20210222 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad sharksDetection SHARKSv20210421 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad ultravistaDetection ULTRAVISTADR4 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad ultravistaMapRemeasurement ULTRAVISTADR4 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad vhsDetection VHSDR2 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vhsDetection VHSDR3 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSDR4 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSDR5 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSDR6 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20120926 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20130417 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20140409 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20150108 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20160114 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20160507 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20170630 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20180419 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection VHSv20201209 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vhsDetection, vhsListRemeasurement VHSDR1 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad videoDetection VIDEODR2 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad videoDetection VIDEODR3 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad videoDetection VIDEODR4 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad videoDetection VIDEODR5 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad videoDetection VIDEOv20100513 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad videoDetection VIDEOv20111208 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad videoListRemeasurement VIDEOv20100513 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vikingDetection VIKINGDR2 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vikingDetection VIKINGDR3 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGDR4 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGv20111019 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vikingDetection VIKINGv20130417 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGv20140402 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGv20150421 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGv20151230 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGv20160406 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGv20161202 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection VIKINGv20170715 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vikingDetection, vikingListRemeasurement VIKINGv20110714 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vikingMapRemeasurement VIKINGZYSELJv20160909 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad vikingMapRemeasurement VIKINGZYSELJv20170124 Petrosian radius (SE: PETRO_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.

petroRad vmcDetection VMCDR1 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vmcDetection VMCDR2 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCDR3 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCDR4 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCDR5 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20110909 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vmcDetection VMCv20120126 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vmcDetection VMCv20121128 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20130304 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20130805 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20140428 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20140903 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20150309 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20151218 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20160311 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20160822 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20170109 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20170411 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20171101 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20180702 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20181120 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20191212 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20210708 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection VMCv20230816 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vmcDetection, vmcListRemeasurement VMCv20110816 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vmcdeepDetection VMCDEEPv20230713 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vvvDetection VVVDR1 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vvvDetection VVVDR2 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize

petroRad vvvDetection, vvvListRemeasurement VVVv20100531 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

PF_DEC mgcBrightSpec MGC PFr object declination in deg (J2000) float 8

PF_JMK mgcBrightSpec MGC PFr J-K colour from 2MASS real 4

PF_K mgcBrightSpec MGC PFr K magnitude from 2MASS real 4

PF_NAME mgcBrightSpec MGC PFr object name varchar 8

PF_R mgcBrightSpec MGC PFr R magnitude from USNO real 4

PF_RA mgcBrightSpec MGC PFr object right ascension in deg (J2000) float 8

PF_Z mgcBrightSpec MGC PFr redshift real 4

PF_ZQUAL mgcBrightSpec MGC PFr redshift quality tinyint 1

pFlag rosat_bsc, rosat_fsc ROSAT possible problem with position determination varchar 1 meta.code

pflag tycho2 GAIADR1 Mean position flag varchar 1 meta.code

pGalaxy sharksSource SHARKSv20210222 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy sharksSource SHARKSv20210421 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTADR4 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSDR1 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSDR2 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSDR3 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSDR4 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSDR5 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSDR6 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20120926 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20130417 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20140409 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20150108 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20160114 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20160507 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20170630 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20180419 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vhsSource VHSv20201209 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy videoSource VIDEODR2 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy videoSource VIDEODR3 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy videoSource VIDEODR4 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy videoSource VIDEODR5 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy videoSource VIDEOv20100513 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy videoSource VIDEOv20111208 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGDR2 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGDR3 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGDR4 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20110714 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20111019 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20130417 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20140402 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20150421 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20151230 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20160406 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20161202 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingSource VIKINGv20170715 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCDR2 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCDR3 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCDR4 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCDR5 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20110816 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20110909 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20120126 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20121128 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20130304 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20130805 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20140428 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20140903 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20150309 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20151218 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20160311 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20160822 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20170109 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20170411 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20171101 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20180702 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20181120 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20191212 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20210708 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource VMCv20230816 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcSource, vmcSynopticSource VMCDR1 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vvvSource VVVDR2 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vvvSource VVVDR5 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vvvSource VVVv20100531 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vvvSource VVVv20110718 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pGalaxy vvvSource, vvvSynopticSource VVVDR1 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

ph_qual allwise_sc WISE Photometric quality flag. Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio. varchar 4

A - Source is detected in this band with a flux signal-to-noise ratio w?snr>10.

B - Source is detected in this band with a flux signal-to-noise ratio 3<w?snr<10.

C - Source is detected in this band with a flux signal-to-noise ratio 2<w?snr<3.

U - Upper limit on magnitude. Source measurement has w?snr<2. The profile-fit magnitude w?mpro is a 95% confidence upper limit.

X - A profile-fit measurement was not possible at this location in this band. The value of w?mpro and w?sigmpro will be "null" in this band.

Z - A profile-fit source flux measurement was made at this location, but the flux uncertainty could not be measured. The value of w?sigmpro will be "null" in this band. The value of w?mpro will be "null" if the measured flux, w?flux, is negative, but will not be "null" if the flux is positive. If a non-null magnitude is present, it corresponds to the true flux, and not the 95% confidence upper limit. This occurs for a small number of sources found in a narrow range of ecliptic longitude which were covered by a large number of saturated pixels from 3-Band Cryo single-exposures.

ph_qual twomass_psc TWOMASS Photometric quality flag. varchar 3 meta.code.qual

ph_qual twomass_sixx2_psc TWOMASS flag indicating photometric quality of source varchar 3

ph_qual wise_allskysc WISE Photometric quality flag.
Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio. char 4

ph_qual wise_prelimsc WISE Photometric quality flag
Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio char 4

ph_qual_ALLWISE ravedr5Source RAVE photometric quality of each band (A=highest, U=upper limit) varchar 5 meta.code_mag

phaRange rosat_bsc, rosat_fsc ROSAT PHA range with highest detection likelihood varchar 1 meta.code

pHeight sharksDetection SHARKSv20210222 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight sharksDetection SHARKSv20210421 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight ultravistaDetection, ultravistaMapRemeasurement ULTRAVISTADR4 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSDR2 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSDR3 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSDR4 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSDR5 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSDR6 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20120926 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20130417 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20140409 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20150108 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20160114 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20160507 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20170630 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20180419 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection VHSv20201209 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vhsDetection, vhsListRemeasurement VHSDR1 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight videoDetection VIDEODR2 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight videoDetection VIDEODR3 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight videoDetection VIDEODR4 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight videoDetection VIDEODR5 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight videoDetection VIDEOv20100513 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight videoDetection VIDEOv20111208 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight videoListRemeasurement VIDEOv20100513 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGDR2 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGDR3 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGDR4 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20111019 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20130417 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20140402 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20150421 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20151230 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20160406 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20161202 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection VIKINGv20170715 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingDetection, vikingListRemeasurement VIKINGv20110714 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingMapRemeasurement VIKINGZYSELJv20160909 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vikingMapRemeasurement VIKINGZYSELJv20170124 Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCDR1 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCDR2 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCDR3 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCDR4 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCDR5 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20110909 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20120126 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20121128 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20130304 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20130805 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20140428 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20140903 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20150309 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20151218 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20160311 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20160822 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20170109 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20170411 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20171101 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20180702 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20181120 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20191212 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20210708 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection VMCv20230816 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcDetection, vmcListRemeasurement VMCv20110816 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vmcdeepDetection VMCDEEPv20230713 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vvvDetection VVVDR1 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vvvDetection VVVDR2 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vvvDetection, vvvListRemeasurement VVVv20100531 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeightErr sharksDetection SHARKSv20210222 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr sharksDetection SHARKSv20210421 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr ultravistaDetection, ultravistaMapRemeasurement ULTRAVISTADR4 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr vhsDetection VHSDR2 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSDR3 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSDR4 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSDR5 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSDR6 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20120926 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20130417 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20140409 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20150108 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20160114 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20160507 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20170630 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20180419 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection VHSv20201209 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vhsDetection, vhsListRemeasurement VHSDR1 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr videoDetection VIDEODR2 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr videoDetection VIDEODR3 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr videoDetection VIDEODR4 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr videoDetection VIDEODR5 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr videoDetection VIDEOv20100513 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr videoDetection VIDEOv20111208 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr videoListRemeasurement VIDEOv20100513 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGDR2 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGDR3 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGDR4 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20111019 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20130417 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20140402 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20150421 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20151230 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20160406 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20161202 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection VIKINGv20170715 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingDetection, vikingListRemeasurement VIKINGv20110714 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vikingMapRemeasurement VIKINGZYSELJv20160909 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr vikingMapRemeasurement VIKINGZYSELJv20170124 Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 real 4 ADU stat.error

pHeightErr vmcDetection VMCDR1 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCDR2 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCDR3 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCDR4 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCDR5 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20110909 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20120126 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20121128 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20130304 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20130805 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20140428 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20140903 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20150309 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20151218 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20160311 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20160822 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20170109 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20170411 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20171101 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20180702 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20181120 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20191212 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20210708 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection VMCv20230816 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcDetection, vmcListRemeasurement VMCv20110816 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vmcdeepDetection VMCDEEPv20230713 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vvvDetection VVVDR1 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vvvDetection VVVDR2 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vvvDetection, vvvListRemeasurement VVVv20100531 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

phi21 ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource OGLE Fourier coefficient phi_21 real 4 stat.param

phi21_g cepheid, rrlyrae GAIADR1 Fourier decomposition parameter phi21G: phi2 - 2*phi1 (for G band) float 8 stat.Fourier

phi21_g_error cepheid, rrlyrae GAIADR1 Uncertainty on Fourier decomposition parameter phi21G float 8 stat.error

phi31 ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource OGLE Fourier coefficient phi_31 real 4 stat.param

phi_opt twomass_psc TWOMASS Position angle on the sky of the vector from the the associated optical source to the TWOMASS source position, in degrees East of North. smallint 2 degrees pos.posAng

phot_bp_mean_flux gaia_source GAIADR2 Integrated BP mean flux float 8 electrons/s phot.flux;stat.mean

phot_bp_mean_flux gaia_source GAIAEDR3 Integrated BP mean flux float 8 electrons/s phot.flux;stat.mean

phot_bp_mean_flux_error gaia_source GAIADR2 Standard error on the integrated BP mean flux float 8 electrons/s stat.error;phot.flux;stat.mean

phot_bp_mean_flux_error gaia_source GAIAEDR3 Standard error on the integrated BP mean flux real 4 electrons/s stat.error;phot.flux;stat.mean

phot_bp_mean_flux_over_error gaia_source GAIADR2 Integrated mean BP flux divided by its standard error real 4 arith.ratio

phot_bp_mean_flux_over_error gaia_source GAIAEDR3 Integrated mean BP flux divided by its standard error real 4 arith.ratio

phot_bp_mean_mag gaia_source GAIADR2 Integrated BP mean magnitude real 4 mag phot.mag;stat.mean

phot_bp_mean_mag gaia_source GAIAEDR3 Integrated BP mean magnitude real 4 mag phot.mag;stat.mean

phot_bp_n_blended_transits gaia_source GAIAEDR3 Number of BP blended transits smallint 2 meta.number

phot_bp_n_contaminated_transits gaia_source GAIAEDR3 Number of BP contaminated transits smallint 2 meta.number

phot_bp_n_obs gaia_source GAIADR2 Number of observations contributing to BP photometry int 4 meta.number

phot_bp_n_obs gaia_source GAIAEDR3 Number of observations contributing to BP photometry smallint 2 meta.number

phot_bp_rp_excess_factor gaia_source GAIADR2 Combined BP and RP excess factor real 4

phot_bp_rp_excess_factor gaia_source GAIAEDR3 Combined BP and RP excess factor real 4 arith.factor;phot.flux;em.opt

phot_flag combo17CDFSSource COMBO17 flags on photometry: bit 0-7 (corresponding to values 0-128) are original SExtractor flags, bit 9-11 set by COMBO-17 photometry, bit 9 indicates only potential problem from bright neighbours or reflexes from the optics (check images), bit 10 indicates uncorrected hot pixels, bit 11 is set interactively when photometry is erroneous smallint 2

phot_g_mean_flux gaia_source GAIADR2 G-band mean flux float 8 electrons/s phot.flux;stat.mean;em.opt

phot_g_mean_flux gaia_source GAIAEDR3 G-band mean flux float 8 electrons/s phot.flux;stat.mean;em.opt

phot_g_mean_flux gaia_source, tgas_source GAIADR1 G-band mean flux float 8 electrons/s phot.flux;stat.mean;em.opt

phot_g_mean_flux_error gaia_source GAIADR2 Error on G-band mean flux float 8 electrons/s stat.error;phot.flux;stat.mean;em.opt

phot_g_mean_flux_error gaia_source GAIAEDR3 Error on G-band mean flux real 4 electrons/s stat.error;phot.flux;stat.mean;em.opt

phot_g_mean_flux_error gaia_source, tgas_source GAIADR1 Error on G-band mean flux float 8 electrons/s stat.error;phot.flux;stat.mean;em.opt

phot_g_mean_flux_error_TGAS ravedr5Source RAVE Error on G-band mean flux from TGAS float 8 e-/s stat.error;phot.flux;stat.mean;em.opt

phot_g_mean_flux_over_error gaia_source GAIADR2 G-band mean flux divided by its standard error float 8 arith.ratio

phot_g_mean_flux_over_error gaia_source GAIAEDR3 G-band mean flux divided by its standard error real 4 arith.ratio

phot_g_mean_flux_TGAS ravedr5Source RAVE Error on G-band mean flux from TGAS float 8 e-/s phot.flux;stat.mean;em.opt

phot_g_mean_mag aux_qso_icrf2_match, gaia_source, tgas_source GAIADR1 G-band mean magnitude float 8 mag phot.mag;stat.mean;em.opt

phot_g_mean_mag gaia_source GAIADR2 G-band mean magnitude real 4 mag phot.mag;stat.mean;em.opt

phot_g_mean_mag gaia_source GAIAEDR3 G-band mean magnitude real 4 mag phot.mag;stat.mean;em.opt

phot_g_mean_mag_TGAS ravedr5Source RAVE G-band mean magnitude from TGAS float 8 mag phot.mag;em.opt.g

phot_g_n_obs gaia_source GAIADR2 Number of observations contributing to G band photometry int 4 meta.number

phot_g_n_obs gaia_source GAIAEDR3 Number of observations contributing to G band photometry smallint 2 meta.number

phot_g_n_obs gaia_source, tgas_source GAIADR1 Number of observations contributing to G band photometry int 4 meta.number

phot_proc_mode gaia_source GAIADR2 Photometry processing mode tinyint 1 meta.code

phot_proc_mode gaia_source GAIAEDR3 Photometry processing mode tinyint 1 meta.code

phot_rp_mean_flux gaia_source GAIADR2 Integrated RP mean flux float 8 electrons/s phot.flux;stat.mean

phot_rp_mean_flux gaia_source GAIAEDR3 Integrated RP mean flux float 8 electrons/s phot.flux;stat.mean

phot_rp_mean_flux_error gaia_source GAIADR2 Standard error on the integrated RP mean flux float 8 electrons/s stat.error;phot.flux;stat.mean

phot_rp_mean_flux_error gaia_source GAIAEDR3 Standard error on the integrated RP mean flux real 4 electrons/s stat.error;phot.flux;stat.mean

phot_rp_mean_flux_over_error gaia_source GAIADR2 Integrated mean RP flux divided by its standard error real 4 arith.ratio

phot_rp_mean_flux_over_error gaia_source GAIAEDR3 Integrated mean RP flux divided by its standard error real 4 arith.ratio

phot_rp_mean_mag gaia_source GAIADR2 Integrated RP mean magnitude real 4 mag phot.mag;stat.mean

phot_rp_mean_mag gaia_source GAIAEDR3 Integrated RP mean magnitude real 4 mag phot.mag;stat.mean

phot_rp_n_blended_transits gaia_source GAIAEDR3 Number of RP blended transits smallint 2 meta.number

phot_rp_n_contaminated_transits gaia_source GAIAEDR3 Number of RP contaminated transits smallint 2 meta.number

phot_rp_n_obs gaia_source GAIADR2 Number of observations contributing to RP photometry int 4 meta.number

phot_rp_n_obs gaia_source GAIAEDR3 Number of observations contributing to RP photometry smallint 2 meta.number

phot_variable_flag gaia_source GAIADR2 Photometric variability flag char 16 meta.code;src.var

phot_variable_flag gaia_source, tgas_source GAIADR1 Photometric variability flag varchar 16 meta.code;src.var

phot_variable_fundam_freq1 variable_summary GAIADR1 Fundamental frequency 1 float 8 /days src.var.pulse

photZPCat MultiframeDetector SHARKSv20210222 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector SHARKSv20210421 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ULTRAVISTADR4 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSDR1 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSDR2 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSDR3 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSDR4 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSDR5 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSDR6 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20120926 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20130417 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20140409 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20150108 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20160114 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20160507 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20170630 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20180419 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VHSv20201209 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIDEODR2 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIDEODR3 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIDEODR4 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIDEODR5 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIDEOv20100513 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIDEOv20111208 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGDR2 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGDR3 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGDR4 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20110714 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20111019 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20130417 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20140402 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20150421 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20151230 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20160406 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20161202 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VIKINGv20170715 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCDEEPv20230713 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCDR1 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCDR2 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCDR3 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCDR4 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCDR5 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20110816 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20110909 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20120126 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20121128 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20130304 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20130805 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20140428 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20140903 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20150309 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20151218 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20160311 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20160822 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20170109 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20170411 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20171101 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20180702 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20181120 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20191212 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20210708 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VMCv20230816 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VVVDR1 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VVVDR2 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VVVDR5 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VVVv20100531 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VVVv20110718 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat PreviousMFDZP SHARKSv20210222 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP SHARKSv20210421 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ULTRAVISTADR4 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSDR1 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSDR2 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSDR3 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSDR4 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSDR5 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSDR6 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20120926 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20130417 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20140409 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20150108 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20160114 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20160507 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20170630 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20180419 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VHSv20201209 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIDEODR2 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIDEODR3 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIDEODR4 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIDEODR5 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIDEOv20100513 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIDEOv20111208 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGDR2 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGDR3 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGDR4 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20110714 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20111019 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20130417 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20140402 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20150421 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20151230 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20160406 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20161202 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VIKINGv20170715 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCDEEPv20230713 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCDR1 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCDR2 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCDR3 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCDR4 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCDR5 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20110816 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20110909 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20120126 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20121128 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20130304 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20130805 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20140428 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20140903 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20150309 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20151218 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20160311 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20160822 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20170109 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20170411 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20171101 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20180702 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20181120 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20191212 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20210708 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VMCv20230816 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VVVDR1 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VVVDR2 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VVVDR5 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VVVv20100531 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VVVv20110718 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector VSAQC Photometric zero point for default extinction for the catalogue data real 4 mags -0.9999995e9 ??

photZPErrCat MultiframeDetector SHARKSv20210222 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector SHARKSv20210421 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ULTRAVISTADR4 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSDR1 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSDR2 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSDR3 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSDR4 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSDR5 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSDR6 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20120926 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20130417 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20140409 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20150108 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20160114 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20160507 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20170630 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20180419 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VHSv20201209 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIDEODR2 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIDEODR3 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIDEODR4 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIDEODR5 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIDEOv20100513 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR}
[Currently set to -1 for WFCAM data.] real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIDEOv20111208 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGDR2 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGDR3 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGDR4 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20110714 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20111019 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20130417 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20140402 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20150421 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20151230 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20160406 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20161202 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VIKINGv20170715 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCDEEPv20230713 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCDR1 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCDR2 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCDR3 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCDR4 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCDR5 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20110816 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20110909 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20120126 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20121128 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20130304 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20130805 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20140428 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20140903 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20150309 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20151218 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20160311 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20160822 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20170109 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20170411 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20171101 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20180702 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20181120 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20191212 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20210708 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VMCv20230816 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VVVDR1 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VVVDR2 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VVVDR5 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VVVv20100531 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR}
[Currently set to -1 for WFCAM data.] real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VVVv20110718 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat PreviousMFDZP SHARKSv20210222 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP SHARKSv20210421 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ULTRAVISTADR4 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSDR1 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSDR2 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSDR3 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSDR4 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSDR5 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSDR6 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20120926 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20130417 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20140409 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20150108 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20160114 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20160507 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20170630 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20180419 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VHSv20201209 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIDEODR2 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIDEODR3 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIDEODR4 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIDEODR5 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIDEOv20100513 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIDEOv20111208 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGDR2 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGDR3 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGDR4 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20110714 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20111019 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20130417 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20140402 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20150421 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20151230 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20160406 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20161202 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VIKINGv20170715 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCDEEPv20230713 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCDR1 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCDR2 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCDR3 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCDR4 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCDR5 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20110816 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20110909 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20120126 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20121128 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20130304 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20130805 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20140428 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20140903 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20150309 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20151218 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20160311 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20160822 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20170109 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20170411 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20171101 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20180702 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20181120 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20191212 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20210708 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VMCv20230816 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VVVDR1 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VVVDR2 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VVVDR5 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VVVv20100531 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VVVv20110718 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector VSAQC Photometric zero point error for the catalogue data real 4 mags -0.9999995e9 ??

picoi Multiframe SHARKSv20210222 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe SHARKSv20210421 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ULTRAVISTADR4 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSDR1 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSDR2 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSDR3 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSDR4 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSDR5 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSDR6 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20120926 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20130417 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20140409 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20150108 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20160114 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20160507 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20170630 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20180419 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VHSv20201209 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIDEODR2 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIDEODR3 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIDEODR4 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIDEODR5 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIDEOv20100513 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIDEOv20111208 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGDR2 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGDR3 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGDR4 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20110714 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20111019 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20130417 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20140402 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20150421 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20151230 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20160406 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20161202 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VIKINGv20170715 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCDEEPv20230713 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCDR1 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCDR2 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCDR3 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCDR4 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCDR5 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20110816 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20110909 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20120126 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20121128 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20130304 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20130805 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20140428 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20140903 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20150309 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20151218 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20160311 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20160822 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20170109 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20170411 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20171101 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20180702 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20181120 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20191212 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20210708 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VMCv20230816 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VVVDR1 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VVVDR2 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VVVDR5 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VVVv20100531 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VVVv20110718 PI-COI name. {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC PI-COI name. varchar 64 NONE

PID_R spectra SIXDF PID number read from R frame int 4

PID_V spectra SIXDF PID number read from V frame int 4

PIDL15 akari_lmc_psa_v1, akari_lmc_psc_v1 AKARI Observing Pointing identifier char 9 9999999.9

PIDL24 akari_lmc_psa_v1, akari_lmc_psc_v1 AKARI Observing Pointing identifier char 9 9999999.9

PIDN3 akari_lmc_psa_v1, akari_lmc_psc_v1 AKARI Observing Pointing identifier char 9 9999999.9

PIDS11 akari_lmc_psa_v1, akari_lmc_psc_v1 AKARI Observing Pointing identifier char 9 9999999.9

PIDS7 akari_lmc_psa_v1, akari_lmc_psc_v1 AKARI Observing Pointing identifier char 9 9999999.9

PIVOT_R spectra SIXDF R pivot number smallint 2

PIVOT_V spectra SIXDF V pivot number smallint 2

Pix_x_I denisDR3Source DENIS Pixel x position in I band float 8 pix

Pix_x_J denisDR3Source DENIS Pixel x position in J band float 8 pix

Pix_x_K denisDR3Source DENIS Pixel x position in K band float 8 pix

Pix_y_I denisDR3Source DENIS Pixel y position in I band float 8 pix

Pix_y_J denisDR3Source DENIS Pixel y position in J band float 8 pix

Pix_y_K denisDR3Source DENIS Pixel y position in K band float 8 pix

pixelID vvvBulge3DExtinctVals EXTINCT UID of the pixel int 4 meta.id

pixelID vvvBulgeExtMapCoords EXTINCT UID of the 2D pixel int 4 meta.id;meta.main

pixelSize RequiredMosaic SHARKSv20210222 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic SHARKSv20210421 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic ULTRAVISTADR4 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSDR1 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSDR2 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSDR3 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSDR4 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSDR5 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSDR6 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20120926 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20130417 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20150108 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20160114 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20160507 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20170630 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20180419 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VHSv20201209 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIDEODR2 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIDEODR3 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIDEODR4 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIDEODR5 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIDEOv20100513 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIDEOv20111208 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGDR2 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGDR3 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGDR4 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20110714 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20111019 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20130417 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20150421 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20151230 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20160406 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20161202 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VIKINGv20170715 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCDEEPv20230713 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCDR1 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCDR3 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCDR4 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCDR5 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20110816 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20110909 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20120126 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20121128 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20130304 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20130805 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20140428 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20140903 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20150309 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20151218 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20160311 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20160822 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20170109 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20170411 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20171101 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20180702 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20181120 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20191212 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20210708 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VMCv20230816 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VVVDR1 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VVVDR2 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VVVDR5 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VVVv20100531 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaic VVVv20110718 The final pixel size of the mosaic real 4 arcsec ??

pixelSize RequiredMosaicTopLevel SHARKSv20210222 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel SHARKSv20210421 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel ULTRAVISTADR4 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel VHSv20201209 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel VMCDEEPv20230713 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel VMCDR5 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel VMCv20191212 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel VMCv20210708 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel VMCv20230816 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixelSize RequiredMosaicTopLevel VVVDR5 The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??

pixSizeAng ThreeDimExtinctionMaps EXTINCT Angular resolution of extinction map real 4 Arcminutes -0.9999995e9

pixSizeRad ThreeDimExtinctionMaps EXTINCT Radial resolution of extinction map real 4 kpc -0.9999995e9

pJKs vvvPsfDaophotJKsSource VVVDR5 The fraction of the number of "recovered" vs injected stars per (J-Ks) - Ks bin {catalogue TType keyword: p} real 4 -0.9999995e9

PlateNumber ravedr5Source RAVE Number of fieldplate on instrument [1..3] tinyint 1 meta.id;instr.plate

pltScale Detection PS1DR2 Local plate scale at this location. real 4 arcsec/pixel -999

plx hipparcos_new_reduction GAIADR1 Parallax float 8 milliarcseconds pos.parallax

plx vvvParallaxCatalogue VVVDR5 Parallax. These are inverse variance weighted averages across their measured values in both equatorial tangent plane dimensions and from all pawprint sets. {catalogue TType keyword: plx} float 8 mas -999999500.0

pm gaia_source GAIAEDR3 Total proper motion real 4 milliarcsec/year pos.pm;pos.eq

pm vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR5 Total Proper motion {catalogue TType keyword: pm} float 8 mas/yr -999999500.0

pm_de hipparcos_new_reduction GAIADR1 Proper motion in Declination float 8 milliarcseconds/year pos.eq.dec;pos.pm

pm_de tycho2 GAIADR1 Proper motion in Dec real 4 milliarcsec/year pos.eq.dec;pos.pm

pm_dec igsl_source GAIADR1 Proper motion in Dec at catalogue epoch real 4 milliarcsec/year pos.pm;pos.eq.dec

pm_dec_error igsl_source GAIADR1 Error in proper motion in Dec real 4 milliarcsec/year stat.error;pos.pm;pos.eq.dec

pm_ra hipparcos_new_reduction GAIADR1 Proper motion in Right Ascension float 8 milliarcseconds/year pos.eq.ra;pos.pm

pm_ra igsl_source GAIADR1 Proper motion in RA at catalogue epoch real 4 milliarcsec/year pos.pm;pos.eq.ra

pm_ra tycho2 GAIADR1 Proper motion in RA*cos(Dec) real 4 milliarcsec/year pos.eq.ra;pos.pm

pm_ra_error igsl_source GAIADR1 Error in proper motion in RA real 4 milliarcsec/year stat.error;pos.pm;pos.eq.ra

PMAG grs_ngpSource, grs_ranSource, grs_sgpSource TWODFGRS Unmatched raw APM profile integrated mag real 4

pmcode allwise_sc WISE This is a five character string that encodes information about factors that impact the accuracy of the motion estimation. These include the original blend count, whether a blend-swap took place, and the distance in hundredths of an arcsecond between the non-motion position and the motion mean-observation-epoch position. This column is null if a motion solution was not attempted or a valid solution was not found. varchar 5

The format is NQDDD where N is the original blend count, Q is either "Y" or "N" for "yes" or "no" a blend-swap occurred (i.e., the original primary component was not the final primary component), and DDD is the radial distance between the non-motion and motion at mean-observation epoch positions in units of 0.01 arcsec, clipped at 999 (almost 10 arcsec).
For example, a well-behaved source that is not part of a blend and that has similar stationary and motion fit positions would have a pmcode value like "1N008". A source with a questionable motion estimate that is passively deblended (nb=2) and whose stationary-fit and motion position differ by a significant amount would have a pmcode value like "3Y234".

pmcode catwise_2020, catwise_prelim WISE quality of the PM solution varchar 5

pmDE_error_TGAS ravedr5Source RAVE Error of proper motion (DE) float 8 mas/yr stat.error;pos.pm;pos.eq.dec

pmDE_PPMXL ravedr5Source RAVE Proper Motion (Declination) real 4 mas/yr pos.pm

pmDE_TGAS ravedr5Source RAVE Proper motion (Declination) float 8 mas/yr pos.pm;pos.eq.dec

pmDE_TYCHO2 ravedr5Source RAVE Proper motion (Declination) real 4 mas/yr pos.pm;pos.eq.dec

pmDE_UCAC4 ravedr5Source RAVE Proper Motion (Declination) real 4 mas/yr pos.pm

pmDE_USNOB1 ravedr5Source RAVE Proper Motion (Declination) real 4 mas/yr pos.pm

PMDec catwise_2020, catwise_prelim WISE proper motion in dec real 4 arcsec/yr

pmDec ukirtFSstars VIDEOv20100513 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars VIKINGv20110714 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars VVVv20100531 Proper motion in Dec real 4 arcsec per year 0.0

pmdec allwise_sc WISE The apparent motion in declination estimated for this source. This column is null if the motion fit failed to converge or was not attempted. CAUTION: This is the total motion in declination, and not the proper motion. The apparent motion can be significantly affected by parallax for nearby objects. int 4 mas/year

pmdec gaia_source GAIADR2 Proper motion in Declination direction float 8 milliarcsec/year pos.pm;.pos.eq.dec

pmdec gaia_source GAIAEDR3 Proper motion in Declination direction float 8 milliarcsec/year pos.pm;.pos.eq.dec

pmdec gaia_source, tgas_source GAIADR1 Proper motion in Declination direction float 8 milliarcsec/year pos.pm;.pos.eq.dec

pmdec_error gaia_source GAIADR2 Error of proper motion in Declination direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.dec

pmdec_error gaia_source GAIAEDR3 Error of proper motion in Declination direction real 4 milliarcsec/year stat.error;pos.pm;.pos.eq.dec

pmdec_error gaia_source, tgas_source GAIADR1 Error of proper motion in Declination direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.dec

pmdec_pseudocolour_corr gaia_source GAIAEDR3 Correlation between proper motion in declination and pseudocolour real 4 stat.correlation;em.wavenumber;pos.pm;pos.eq.dec

PMRA catwise_2020, catwise_prelim WISE motion in ra real 4 arcsec/yr

pmRA ukirtFSstars VIDEOv20100513 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars VIKINGv20110714 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars VVVv20100531 Proper motion in RA real 4 arcsec per year 0.0

pmra allwise_sc WISE The apparent motion in right ascension estimated for this source. This column is null if the motion fit failed to converge or was not attempted. CAUTION: This is the total motion in right ascension, and not the proper motion. The apparent motion can be significantly affected by parallax for nearby objects. int 4 mas/year

pmra gaia_source GAIADR2 Proper motion in Right Ascension direction float 8 milliarcsec/year pos.pm;.pos.eq.ra

pmra gaia_source GAIAEDR3 Proper motion in Right Ascension direction float 8 milliarcsec/year pos.pm;.pos.eq.ra

pmra gaia_source, tgas_source GAIADR1 Proper motion in Right Ascension direction float 8 milliarcsec/year pos.pm;.pos.eq.ra

pmra_error gaia_source GAIADR2 Error of proper motion in Right Ascension direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.ra

pmra_error gaia_source GAIAEDR3 Error of proper motion in Right Ascension direction real 4 milliarcsec/year stat.error;pos.pm;.pos.eq.ra

pmra_error gaia_source, tgas_source GAIADR1 Error of proper motion in Right Ascension direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.ra

pmRA_error_TGAS ravedr5Source RAVE Error of proper motion (RA) float 8 mas/yr stat.errror;pos.pm;pos.eq.ra

pmra_pmdec_corr gaia_source GAIADR2 Correlation between proper motion in Right Ascension and proper motion in Declination real 4 stat.correlation;pos.pm;pos.eq.ra;pos.pm;pos.eq.dec

pmra_pmdec_corr gaia_source GAIAEDR3 Correlation between proper motion in Right Ascension and proper motion in Declination real 4 stat.correlation;pos.pm;pos.eq.ra;pos.pm;pos.eq.dec

pmra_pmdec_corr gaia_source, tgas_source GAIADR1 Correlation between proper motion in Right Ascension and proper motion in Declination real 4 stat.correlation

pmRA_PPMXL ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

pmra_pseudocolour_corr gaia_source GAIAEDR3 Correlation between proper motion in right ascension and pseudocolour real 4 stat.correlation;em.wavenumber;pos.pm;pos.eq.ra

pmRA_TGAS ravedr5Source RAVE Proper motion (Right Ascension) float 8 mas/yr pos.pm;pos.eq.ra

pmRA_TYCHO2 ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

pmRA_UCAC4 ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

pmRA_USNOB1 ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

PN_1_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 1 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_1_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 Maximum likelihood real 4

PN_1_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 1 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_1_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 flux real 4 erg/cm**2/s

PN_1_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 flux error real 4 erg/cm**2/s

PN_1_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 Count rates real 4 counts/s

PN_1_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 Count rates error real 4 counts/s

PN_1_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 1 vignetting value. real 4

PN_2_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 2 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_2_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 Maximum likelihood real 4

PN_2_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 2 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_2_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 flux real 4 erg/cm**2/s

PN_2_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 flux error real 4 erg/cm**2/s

PN_2_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 Count rates real 4 counts/s

PN_2_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 Count rates error real 4 counts/s

PN_2_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 2 vignetting value. real 4

PN_3_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 3 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_3_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 Maximum likelihood real 4

PN_3_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 3 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_3_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 flux real 4 erg/cm**2/s

PN_3_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 flux error real 4 erg/cm**2/s

PN_3_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 Count rates real 4 counts/s

PN_3_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 Count rates error real 4 counts/s

PN_3_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 3 vignetting value. real 4

PN_4_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 4 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_4_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 Maximum likelihood real 4

PN_4_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 4 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_4_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 flux real 4 erg/cm**2/s

PN_4_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 flux error real 4 erg/cm**2/s

PN_4_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 Count rates real 4 counts/s

PN_4_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 Count rates error real 4 counts/s

PN_4_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 4 vignetting value. real 4

PN_5_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 5 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_5_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 Maximum likelihood real 4

PN_5_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 5 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_5_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 flux real 4 erg/cm**2/s

PN_5_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 flux error real 4 erg/cm**2/s

PN_5_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 Count rates real 4 counts/s

PN_5_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 Count rates error real 4 counts/s

PN_5_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 5 vignetting value. real 4

PN_8_CTS twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Combined band source counts real 4 counts

PN_8_CTS_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Combined band source counts 1 σ error real 4 counts

PN_8_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 Maximum likelihood real 4

PN_8_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 flux real 4 erg/cm**2/s

PN_8_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 flux error real 4 erg/cm**2/s

PN_8_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 Count rates real 4 counts/s

PN_8_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 Count rates error real 4 counts/s

PN_9_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 Maximum likelihood real 4

PN_9_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 flux real 4 erg/cm**2/s

PN_9_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 flux error real 4 erg/cm**2/s

PN_9_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 Count rates real 4 counts/s

PN_9_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 Count rates error real 4 counts/s

PN_CHI2PROB twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM The Chi² probability (based on the null hypothesis) that the source as detected by the PN camera is constant.
The Pearson approximation to Chi² for Poissonian data was used, in which the model is used as the estimator of its own variance . If more than one exposure (that is, time series) is available for this source the smallest value of probability was used. real 4

PN_CHI2PROB xmm3dr4 XMM The Chi² probability (based on the null hypothesis) that the source as detected by the PN camera is constant.
The Pearson approximation to Chi² for Poissonian data was used, in which the model is used as the estimator of its own variance . If more than one exposure (that is, time series) is available for this source the smallest value of probability was used. float 8

PN_FILTER twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM PN filter. The options are Thick, Medium, Thin1, Thin2, and Open, depending on the efficiency of the optical blocking. varchar 6

PN_FILTER xmm3dr4 XMM PN filter. The options are Thick, Medium, Thin1, Thin2, and Open, depending on the efficiency of the optical blocking. varchar 50

PN_FLAG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM PN flag string made of the flags 1 - 12 (counted from left to right) for the PN source detection.
In case where the camera was not used in the source detection a dash is given. In case a source was not detected by the PN the flags are all set to False (default). Flag 10 is not used. varchar 12

PN_FLAG xmm3dr4 XMM PN flag string made of the flags 1 - 12 (counted from left to right) for the PN source detection.
In case where the camera was not used in the source detection a dash is given. In case a source was not detected by the PN the flags are all set to False (default). Flag 10 is not used. varchar 50

PN_FVAR xmm3dr4 XMM The fractional excess variance measured in the PN timeseries of the detection. Where multiple PN exposures exist, it is for the one giving the largest probability of variability (PN_CHI2PROB). This quantity provides a measure of the amplitude of variability in the timeseries, above purely statistical fluctuations. float 8

PN_FVARERR xmm3dr4 XMM The error on the fractional excess variance for the PN timeseries of the detection (PN_FVAR). float 8

PN_HR1 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 1 & 2
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR1_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 1 & 2 real 4

PN_HR2 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 2 & 3
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR2_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 2 & 3 real 4

PN_HR3 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 3 & 4
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR3_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 3 & 4 real 4

PN_HR4 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 4 & 5
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR4_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 4 & 5 real 4

PN_MASKFRAC twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PSF weighted mean of the detector coverage of a detection as derived from the detection mask.
Sources which have less than 0.15 of their PSF covered by the detector are considered as being not detected. real 4

PN_OFFAX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN offaxis angle (the distance between the detection position and the onaxis position on the respective detector).
The offaxis angle for a camera can be larger than 15 arcminutes when the detection is located outside the FOV of that camera. real 4 arcmin

PN_ONTIME twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN ontime value (the total good exposure time (after GTI filtering) of the CCD where the detection is positioned).
If a source position falls into CCD gaps or outside of the detector it will have a NULL given. real 4 s

PN_SUBMODE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM PN observing mode. The options are full frame mode with the full FOV exposed (in two sub-modes), and large window mode with only parts of the FOV exposed. varchar 23

PN_SUBMODE xmm3dr4 XMM PN observing mode. The options are full frame mode with the full FOV exposed (in two sub-modes), and large window mode with only parts of the FOV exposed. varchar 50

pNearH iras_psc IRAS Number of nearby hours-confirmed point sources tinyint 1 meta.number

pNearW iras_psc IRAS Number of nearby weeks-confirmed point sources tinyint 1 meta.number

pNoise sharksSource SHARKSv20210222 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise sharksSource SHARKSv20210421 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTADR4 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSDR1 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSDR2 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSDR3 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSDR4 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSDR5 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSDR6 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20120926 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20130417 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20140409 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20150108 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20160114 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20160507 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20170630 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20180419 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vhsSource VHSv20201209 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise videoSource VIDEODR2 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise videoSource VIDEODR3 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise videoSource VIDEODR4 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise videoSource VIDEODR5 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise videoSource VIDEOv20100513 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise videoSource VIDEOv20111208 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGDR2 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGDR3 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGDR4 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20110714 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20111019 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20130417 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20140402 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20150421 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20151230 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20160406 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20161202 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingSource VIKINGv20170715 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCDR2 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCDR3 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCDR4 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCDR5 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20110816 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20110909 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20120126 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20121128 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20130304 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20130805 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20140428 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20140903 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20150309 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20151218 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20160311 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20160822 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20170109 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20170411 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20171101 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(class_k)=Π_iP(class_k)_i / Σ_kΠ_iP(class_k)_i
where class_k is one of star|galaxy|noise|saturated, and i denotes the i^th single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

pNoise vmcSource VMCv20180702 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1