VSA Browser

Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20240304 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20240304 Schema Browser (other Browser versions).

A B C D E F G H I J K L M
N O P Q R S T U V W X Y Z

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 VMCv20240226 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 VMCv20240226 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 VVVXDR1 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 vmcVariability VMCv20240226 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 VMCv20240226 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VVVDR5 Template file for SWARP parameters varchar 32

paramTemplate RequiredMosaicTopLevel VVVXDR1 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 VMCv20240226 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 VVVXDR1 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 vmcCepheidVariables VMCv20240226 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 vmcEclipsingBinaryVariables VMCv20240226 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

period vmcRRlyraeVariables VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VMCv20240226 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 VVVXDR1 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 VMCv20240226 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 VVVXDR1 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 VMCv20240226 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 VVVXDR1 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 VMCv20240226 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 VVVXDR1 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 VMCv20240226 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 VVVXDR1 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 VMCv20240226 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 VVVXDR1 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 VMCv20240226 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 ??

pixelSize RequiredMosaicTopLevel VVVXDR1 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 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 VMCv20181120 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 VMCv20191212 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 VMCv20210708 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 VMCv20230816 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 VMCv20240226 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, vmcSynopticSource VMCDR1 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 vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 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 vvvSource VVVDR2 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 vvvSource VVVDR5 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 vvvSource VVVv20100531 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 vvvSource VVVv20110718 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 vvvSource, vvvSynopticSource VVVDR1 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.

polFlux nvssSource NVSS Integrated linearly polarized flux density real 4 mJy PHOT_FLUX_LINEAR

polPA nvssSource NVSS [-90,90] The position angle of polFlux real 4 degress POS_POS-EQ

pos iras_asc IRAS Position Angle from IRAS Source to Association (E of N) smallint 2 degrees pos.posAng

posAng iras_psc IRAS Uncertainty ellipse position angle (East of North) smallint 2 degrees pos.posAng

posAngle CurrentAstrometry SHARKSv20210222 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry SHARKSv20210421 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ULTRAVISTADR4 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSDR1 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSDR2 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSDR3 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSDR4 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSDR5 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSDR6 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20120926 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20130417 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20140409 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20150108 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20160114 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20160507 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20170630 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20180419 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VHSv20201209 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIDEODR2 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIDEODR3 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIDEODR4 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIDEODR5 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIDEOv20100513 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIDEOv20111208 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGDR2 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGDR3 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGDR4 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20110714 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20111019 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20130417 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20140402 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20150421 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20151230 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20160406 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20161202 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VIKINGv20170715 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCDEEPv20230713 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCDR1 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCDR2 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCDR3 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCDR4 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCDR5 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20110816 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20110909 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20120126 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20121128 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20130304 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20130805 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20140428 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20140903 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20150309 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20151218 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20160311 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20160822 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20170109 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20170411 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20171101 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20180702 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20181120 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20191212 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20210708 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20230816 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VMCv20240226 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VVVDR1 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VVVDR2 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VVVDR5 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VVVXDR1 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VVVv20100531 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VVVv20110718 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle Detection PS1DR2 Position angle (sky-to-chip) at this location. real 4 degrees -999

posAngle RequiredMosaic SHARKSv20210421 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic ULTRAVISTADR4 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSDR1 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSDR2 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSDR3 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSDR4 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSDR5 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSDR6 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20120926 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20130417 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20150108 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20160114 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20160507 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20170630 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20180419 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VHSv20201209 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIDEODR2 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIDEODR3 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIDEODR4 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIDEODR5 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIDEOv20111208 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGDR2 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGDR3 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGDR4 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20110714 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20111019 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20130417 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20150421 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20151230 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20160406 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20161202 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VIKINGv20170715 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCDEEPv20230713 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCDR1 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCDR3 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCDR4 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCDR5 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20110816 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20110909 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20120126 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20121128 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20130304 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20130805 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20140428 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20140903 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20150309 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20151218 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20160311 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20160822 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20170109 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20170411 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20171101 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20180702 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20181120 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20191212 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20210708 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20230816 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VMCv20240226 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VVVDR1 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VVVDR2 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VVVDR5 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VVVXDR1 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic VVVv20110718 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredMosaic, RequiredRegion, RequiredStack, RequiredTile SHARKSv20210222 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngleTolerance Programme SHARKSv20210222 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme SHARKSv20210421 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ULTRAVISTADR4 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSDR1 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSDR2 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSDR3 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSDR4 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSDR5 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSDR6 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20120926 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20130417 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20150108 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20160114 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20160507 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20170630 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20180419 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VHSv20201209 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIDEODR2 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIDEODR3 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIDEODR4 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIDEODR5 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIDEOv20111208 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGDR2 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGDR3 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGDR4 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20110714 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20111019 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20130417 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20150421 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20151230 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20160406 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20161202 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VIKINGv20170715 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCDEEPv20230713 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCDR1 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCDR3 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCDR4 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCDR5 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20110816 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20110909 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20120126 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20121128 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20130304 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20130805 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20140428 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20140903 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20150309 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20151218 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20160311 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20160822 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20170109 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20170411 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20171101 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20180702 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20181120 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20191212 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20210708 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20230816 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VMCv20240226 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VSAQC The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VVVDR1 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VVVDR2 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VVVDR5 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VVVXDR1 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VVVv20110718 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

POSCOROK xmm3dr4 XMM Signifies whether catcorr obtained a statistically reliable solution or not (0 = False, 1 = True). This parameter is redundant in the sense that if REFCAT is positive, then a reliable solution was considered to have been found. bit 1

POSERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Total position uncertainty in arcseconds calculated by combining the statistical error RADEC_ERR and the systematic error SYSERR as follows: POSERR = SQRT ( RADEC_ERR² + SYSERR² ). real 4 arcsec

posflg tycho2 GAIADR1 Type of Tycho2 solution varchar 1 meta.id;stat.fit

posMeanChisq ObjectThin PS1DR2 Reduced chi squared value of mean position. real 4 -999 stat.stdev

posstdev decapsSource DECAPS Standard deviation in position of object between different detections {catalogue TType keyword: posstdev} real 4 arcsec stat.error;pos.eq

posstdev_ok decapsSource DECAPS Standard deviation in position of object between different detections {catalogue TType keyword: posstdev_ok} real 4 arcsec stat.error;pos.eq

ppErrBits sharksDetection SHARKSv20210222 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits sharksDetection SHARKSv20210421 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits ultravistaDetection, ultravistaMapRemeasurement ULTRAVISTADR4 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits ultravistaMapRemeasAver ULTRAVISTADR4 additional WFAU post-processing error bits based on combining average pawprint and tile flagging int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSDR1 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSDR2 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSDR3 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSDR4 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSDR5 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSDR6 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20120926 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20130417 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20140409 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20150108 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20160114 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20160507 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20170630 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20180419 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsDetection VHSv20201209 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vhsListRemeasurement VHSDR1 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBits videoDetection VIDEODR2 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits videoDetection VIDEODR3 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits videoDetection VIDEODR4 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits videoDetection VIDEODR5 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits videoDetection VIDEOv20100513 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits videoDetection VIDEOv20111208 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits videoListRemeasurement VIDEOv20100513 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBits vikingDetection VIKINGDR2 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGDR3 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGDR4 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20110714 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20111019 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20130417 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20140402 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20150421 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20151230 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20160406 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20161202 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingDetection VIKINGv20170715 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingListRemeasurement VIKINGv20110714 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBits vikingListRemeasurement VIKINGv20111019 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBits vikingMapRemeasAver VIKINGZYSELJv20160909 additional WFAU post-processing error bits based on combining average pawprint and tile flagging int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingMapRemeasAver VIKINGZYSELJv20170124 additional WFAU post-processing error bits based on combining average pawprint and tile flagging int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingMapRemeasurement VIKINGZYSELJv20160909 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vikingMapRemeasurement VIKINGZYSELJv20170124 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCDR1 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCDR2 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCDR3 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCDR4 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCDR5 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20110816 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20110909 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20120126 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20121128 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20130304 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20130805 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20140428 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20140903 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20150309 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20151218 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20160311 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20160822 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20170109 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20170411 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20171101 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20180702 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20181120 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20191212 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20210708 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20230816 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcDetection VMCv20240226 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vmcListRemeasurement VMCv20110816 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBits vmcListRemeasurement VMCv20110909 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBits vmcdeepDetection VMCDEEPv20230713 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vvvDetection VVVDR1 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vvvDetection VVVDR2 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vvvDetection VVVv20100531 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vvvListRemeasurement VVVv20100531 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBits vvvListRemeasurement VVVv20110718 additional WFAU post-processing error bits int 4 0 meta.code

ppErrBitsStatus MapFrameStatus SHARKSv20210222 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus SHARKSv20210421 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus ULTRAVISTADR4 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VHSv20201209 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VMCDEEPv20230713 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VMCDR5 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VMCv20191212 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VMCv20210708 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VMCv20230816 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VMCv20240226 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VVVDR5 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus MapFrameStatus VVVXDR1 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. int 4 0

ppErrBitsStatus ProgrammeFrame SHARKSv20210222 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame SHARKSv20210421 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ULTRAVISTADR4 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSDR1 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSDR2 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSDR3 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSDR4 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSDR5 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSDR6 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20120926 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20130417 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20140409 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20150108 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20160114 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20160507 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20170630 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20180419 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VHSv20201209 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIDEODR2 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIDEODR3 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIDEODR4 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIDEODR5 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIDEOv20100513 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIDEOv20111208 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGDR2 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGDR3 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGDR4 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20110714 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20111019 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20130417 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20140402 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20150421 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20151230 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20160406 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20161202 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VIKINGv20170715 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCDEEPv20230713 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCDR1 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCDR2 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCDR3 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCDR4 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCDR5 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20110816 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20110909 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20120126 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20121128 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20130304 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20130805 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20140428 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20140903 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20150309 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20151218 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20160311 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20160822 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20170109 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20170411 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20171101 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20180702 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20181120 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20191212 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20210708 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20230816 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VMCv20240226 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VSAQC Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VVVDR1 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VVVDR2 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VVVDR5 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VVVXDR1 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VVVv20100531 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VVVv20110718 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

previewv Multiframe SHARKSv20210222 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe SHARKSv20210421 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ULTRAVISTADR4 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSDR1 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSDR2 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSDR3 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSDR4 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSDR5 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSDR6 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20120926 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20130417 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20140409 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20150108 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20160114 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20160507 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20170630 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20180419 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VHSv20201209 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIDEODR2 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIDEODR3 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIDEODR4 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIDEODR5 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIDEOv20100513 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIDEOv20111208 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGDR2 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGDR3 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGDR4 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20110714 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20111019 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20130417 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20140402 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20150421 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20151230 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20160406 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20161202 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VIKINGv20170715 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCDEEPv20230713 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCDR1 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCDR2 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCDR3 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCDR4 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCDR5 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20110816 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20110909 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20120126 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20121128 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20130304 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20130805 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20140428 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20140903 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20150309 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20151218 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20160311 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20160822 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20170109 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20170411 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20171101 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20180702 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20181120 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20191212 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20210708 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20230816 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VMCv20240226 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VVVDR1 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VVVDR2 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VVVDR5 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VVVXDR1 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VVVv20100531 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VVVv20110718 Version of previ {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Version of previ varchar 64 NONE

priam_flags gaia_source GAIADR2 Flags from Apsis-Priam analysis bigint 8 meta.code

priFlgLb rosat_bsc, rosat_fsc ROSAT priority flag L-broad tinyint 1 meta.code

priFlgLh rosat_bsc, rosat_fsc ROSAT priority flag L-hard tinyint 1 meta.code

priFlgLs rosat_bsc, rosat_fsc ROSAT priority flag L-soft tinyint 1 meta.code

priFlgMb rosat_bsc, rosat_fsc ROSAT priority flag M-broad tinyint 1 meta.code

priFlgMh rosat_bsc, rosat_fsc ROSAT priority flag M-hard tinyint 1 meta.code

priFlgMs rosat_bsc, rosat_fsc ROSAT priority flag M-soft tinyint 1 meta.code

primaryDetection StackObjectAttributes, StackObjectThin PS1DR2 Identifies if this row is the primary stack detection. tinyint 1 255

PRIORITY agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss, supercos, twomass SIXDF survey weight smallint 2

priOrSec sharksSource SHARKSv20210222 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec sharksSource SHARKSv20210421 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTADR4 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSDR1 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSDR2 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSDR3 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSDR4 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSDR5 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSDR6 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20120926 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20130417 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20140409 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20150108 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20160114 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20160507 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20170630 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20180419 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSource VHSv20201209 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vhsSourceRemeasurement VHSDR1 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec videoSource VIDEODR2 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec videoSource VIDEODR3 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec videoSource VIDEODR4 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec videoSource VIDEODR5 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec videoSource VIDEOv20100513 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec videoSource VIDEOv20111208 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec videoSourceRemeasurement VIDEOv20100513 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGDR2 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGDR3 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGDR4 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20110714 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20111019 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20130417 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20140402 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20150421 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20151230 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20160406 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20161202 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSource VIKINGv20170715 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSourceRemeasurement VIKINGv20110714 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingSourceRemeasurement VIKINGv20111019 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcPsfSource VMCv20180702 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcPsfSource VMCv20181120 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcPsfSource VMCv20191212 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcPsfSource VMCv20210708 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcPsfSource VMCv20230816 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcPsfSource VMCv20240226 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcPsfSource, vmcSource VMCDR5 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcSourceRemeasurement VMCv20110816 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcSourceRemeasurement VMCv20110909 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vmcdeepSource VMCDEEPv20230713 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vvvPsfDaophotJKsSource, vvvPsfDophotZYJHKsSource, vvvSource VVVDR5 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vvvSourceRemeasurement VVVv20100531 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vvvSourceRemeasurement VVVv20110718 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

PROB grs_ngpSource, grs_ranSource, grs_sgpSource TWODFGRS psi classification parameter: for eyeballed galaxies: psi*1000 = (10000 + abs(jon) + psi*100); for eyeballed non-galaxies: psi*1000 = -(10000 + abs(jon) + psi*100) real 4

prob smashdr2_deep, smashdr2_object SMASH Average Source Extractor stellaricity probability value real 4

prob smashdr2_source SMASH Source Extractor stellaricity probability value (0∼galaxy, 1∼star) real 4

probGal wiseScosSvm WISExSCOSPZ Probability of being a galaxy {image primary HDU keyword: pgal} real 4 -0.9999995e9

probQso wiseScosSvm WISExSCOSPZ Probability of being a QSO {image primary HDU keyword: pqso} real 4 -0.9999995e9

probStar wiseScosSvm WISExSCOSPZ Probability of being a star {image primary HDU keyword: pstar} real 4 -0.9999995e9

processingVersion Detection, StackObjectThin PS1DR2 Data release version. tinyint 1

processingVersion ObjectThin PS1DR2 Data release version. tinyint 1 meta.id;meta.software

productID EpochFrameStatus SHARKSv20210421 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus ULTRAVISTADR4 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VHSDR5 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VHSDR6 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VHSv20160114 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VHSv20160507 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VHSv20170630 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VHSv20180419 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VHSv20201209 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VIKINGv20151230 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VIKINGv20160406 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VIKINGv20161202 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VIKINGv20170715 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCDEEPv20230713 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCDR4 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCDR5 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20151218 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20160311 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20160822 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20170109 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20170411 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20171101 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20180702 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20181120 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20191212 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20210708 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20230816 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VMCv20240226 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VVVDR5 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VVVXDR1 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus, ProgrammeFrame SHARKSv20210222 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID ExternalProduct SHARKSv20210421 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct ULTRAVISTADR4 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VHSv20180419 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VHSv20201209 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCDEEPv20230713 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCDR5 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCv20180702 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCv20181120 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCv20191212 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCv20210708 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCv20230816 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VMCv20240226 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VVVDR5 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct VVVXDR1 unique productID over all programmes int 4 meta.id;meta.main

productID ExternalProduct, ExternalProductCatalogue SHARKSv20210222 unique productID over all programmes int 4 meta.id;meta.main

productID RequiredDiffImage SHARKSv20210222 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage SHARKSv20210421 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ULTRAVISTADR4 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSDR1 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSDR2 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSDR3 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSDR4 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSDR5 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSDR6 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20120926 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20130417 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20150108 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20160114 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20160507 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20170630 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20180419 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VHSv20201209 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIDEODR2 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIDEODR3 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIDEODR4 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIDEODR5 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIDEOv20100513 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIDEOv20111208 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGDR2 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGDR3 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGDR4 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20110714 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20111019 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20130417 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20150421 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20151230 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20160406 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20161202 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VIKINGv20170715 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCDEEPv20230713 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCDR1 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCDR3 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCDR4 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCDR5 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20110816 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20110909 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20120126 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20121128 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20130304 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20130805 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20140428 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20140903 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20150309 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20151218 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20160311 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20160822 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20170109 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20170411 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20171101 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20180702 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20181120 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20191212 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20210708 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20230816 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VMCv20240226 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VVVDR1 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VVVDR2 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VVVDR5 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VVVXDR1 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VVVv20100531 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VVVv20110718 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredMergeLogMultiEpoch SHARKSv20210421 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch ULTRAVISTADR4 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VHSDR5 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VHSDR6 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VHSv20160114 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VHSv20160507 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VHSv20170630 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VHSv20180419 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VHSv20201209 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VIKINGv20151230 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VIKINGv20160406 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VIKINGv20161202 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VIKINGv20170715 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCDEEPv20230713 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCDR4 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCDR5 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20151218 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20160311 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20160822 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20170109 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20170411 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20171101 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20180702 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20181120 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20191212 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20210708 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20230816 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VMCv20240226 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VVVDR5 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VVVXDR1 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch, RequiredStack, RequiredTile SHARKSv20210222 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMosaic SHARKSv20210222 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic SHARKSv20210421 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic ULTRAVISTADR4 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSDR1 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSDR2 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSDR3 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSDR4 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSDR5 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSDR6 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20120926 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20130417 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20150108 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20160114 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20160507 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20170630 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20180419 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VHSv20201209 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIDEODR2 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIDEODR3 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIDEODR4 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIDEODR5 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIDEOv20100513 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIDEOv20111208 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGDR2 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGDR3 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGDR4 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20110714 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20111019 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20130417 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20150421 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20151230 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20160406 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20161202 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VIKINGv20170715 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCDEEPv20230713 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCDR1 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCDR3 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCDR4 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCDR5 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20110816 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20110909 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20120126 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20121128 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20130304 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20130805 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20140428 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20140903 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20150309 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20151218 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20160311 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20160822 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20170109 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20170411 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20171101 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20180702 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20181120 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20191212 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20210708 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20230816 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VMCv20240226 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VVVDR1 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VVVDR2 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VVVDR5 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VVVXDR1 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VVVv20100531 A unique identifier assigned to each required mosaic product entry int 4 ??

productID RequiredMosaic VVVv20110718 A unique identifier assigned to each required mosaic product entry int 4 ??

productType EpochFrameStatus SHARKSv20210222 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus SHARKSv20210421 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus ULTRAVISTADR4 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VHSDR5 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VHSDR6 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VHSv20160114 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VHSv20160507 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VHSv20170630 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VHSv20180419 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VHSv20201209 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VIKINGv20151230 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VIKINGv20160406 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VIKINGv20161202 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VIKINGv20170715 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCDEEPv20230713 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCDR4 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCDR5 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20151218 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20160311 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20160822 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20170109 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20170411 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20171101 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20180702 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20181120 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20191212 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20210708 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20230816 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VMCv20240226 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VVVDR5 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VVVXDR1 Product type (stack,tile,mosaic) varchar 16 NONE

productType ExternalProduct SHARKSv20210222 The product type within the imported directory varchar 16 ??

productType ExternalProduct SHARKSv20210421 The product type within the imported directory varchar 16 ??

productType ExternalProduct ULTRAVISTADR4 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSDR4 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSDR5 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSDR6 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSv20150108 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSv20160114 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSv20160507 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSv20170630 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSv20180419 The product type within the imported directory varchar 16 ??

productType ExternalProduct VHSv20201209 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIDEODR4 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIDEODR5 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIDEOv20111208 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIKINGDR4 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIKINGv20150421 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIKINGv20151230 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIKINGv20160406 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIKINGv20161202 The product type within the imported directory varchar 16 ??

productType ExternalProduct VIKINGv20170715 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCDEEPv20230713 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCDR3 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCDR4 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCDR5 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20140428 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20140903 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20150309 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20151218 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20160311 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20160822 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20170109 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20170411 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20171101 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20180702 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20181120 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20191212 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20210708 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20230816 The product type within the imported directory varchar 16 ??

productType ExternalProduct VMCv20240226 The product type within the imported directory varchar 16 ??

productType ExternalProduct VVVDR5 The product type within the imported directory varchar 16 ??

productType ExternalProduct VVVXDR1 The product type within the imported directory varchar 16 ??

productType ExternalProduct, ExternalProductCatalogue VHSDR3 The product type within the imported directory varchar 16 ??

productType RequiredListDrivenProduct VHSv20130417 product type of file to be extracted (stack, tile, mosaic) varchar 8 NONE

productType RequiredListDrivenProduct VIKINGv20130417 product type of file to be extracted (stack, tile, mosaic) varchar 8 NONE

productType RequiredListDrivenProduct VMCv20130805 product type of file to be extracted (stack, tile, mosaic) varchar 8 NONE

productType RequiredMergeLogMultiEpoch SHARKSv20210222 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch SHARKSv20210421 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch ULTRAVISTADR4 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VHSDR5 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VHSDR6 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VHSv20160114 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VHSv20160507 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VHSv20170630 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VHSv20180419 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VHSv20201209 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VIKINGv20151230 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VIKINGv20160406 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VIKINGv20161202 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VIKINGv20170715 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCDEEPv20230713 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCDR4 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCDR5 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20151218 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20160311 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20160822 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20170109 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20170411 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20171101 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20180702 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20181120 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20191212 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20210708 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20230816 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VMCv20240226 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VVVDR5 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VVVXDR1 Product type (stack,tile,mosaic) varchar 16

PROGID agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss, supercos, twomass SIXDF programme ID smallint 2

PROGID target SIXDF highest priority programme ID smallint 2

PROGID_R spectra SIXDF programme ID in R frame varchar 32

PROGID_V spectra SIXDF programme ID in V frame varchar 32

programmeID CombinedFilters SHARKSv20210421 the unique programme ID int 4 meta.id

programmeID CombinedFilters ULTRAVISTADR4 the unique programme ID int 4 meta.id

programmeID CombinedFilters VHSv20201209 the unique programme ID int 4 meta.id

programmeID CombinedFilters VMCDEEPv20230713 the unique programme ID int 4 meta.id

programmeID CombinedFilters VMCDR5 the unique programme ID int 4 meta.id

programmeID CombinedFilters VMCv20191212 the unique programme ID int 4 meta.id

programmeID CombinedFilters VMCv20210708 the unique programme ID int 4 meta.id

programmeID CombinedFilters VMCv20230816 the unique programme ID int 4 meta.id

programmeID CombinedFilters VMCv20240226 the unique programme ID int 4 meta.id

programmeID CombinedFilters VVVDR5 the unique programme ID int 4 meta.id

programmeID CombinedFilters VVVXDR1 the unique programme ID int 4 meta.id

programmeID CombinedFilters, ExternalProduct, ExternalProductCatalogue, MapFilterLupt, ProductLinks, ProgrammeCurationHistory, ProgrammeTable, RegionFieldLinks, RequiredDiffImage, RequiredFilters, RequiredMapAverages, RequiredMatchedApertureProduct, RequiredMergeLogMultiEpoch, RequiredMosaic, RequiredMosaicTopLevel, RequiredNeighbours, RequiredRegion, RequiredStack, RequiredTile, SExtractorInputParams SHARKSv20210222 the unique programme ID int 4 meta.id

programmeID EpochFrameStatus SHARKSv20210421 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus ULTRAVISTADR4 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VHSDR5 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VHSDR6 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VHSv20160114 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VHSv20160507 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VHSv20170630 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VHSv20180419 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VHSv20201209 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VIKINGv20151230 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VIKINGv20160406 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VIKINGv20161202 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VIKINGv20170715 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCDEEPv20230713 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCDR4 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCDR5 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20151218 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20160311 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20160822 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20170109 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20170411 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20171101 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20180702 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20181120 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20191212 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20210708 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20230816 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VMCv20240226 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VVVDR5 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VVVXDR1 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus, ProgrammeFrame SHARKSv20210222 VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID MapFrameStatus SHARKSv20210222 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus SHARKSv20210421 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus ULTRAVISTADR4 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VHSv20201209 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VMCDEEPv20230713 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VMCDR5 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VMCv20191212 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VMCv20210708 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VMCv20230816 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VMCv20240226 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VVVDR5 WSA assigned programme UID int 4 -99999999 meta.id

programmeID MapFrameStatus VVVXDR1 WSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams SHARKSv20210421 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams VHSv20201209 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams VMCDEEPv20230713 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams VMCv20210708 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams VMCv20230816 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams VMCv20240226 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams VVVDR5 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams VVVXDR1 VSA assigned programme UID int 4 -99999999 meta.id

programmeID PreviousCalibParams, ProblemFrames SHARKSv20210222 VSA assigned programme UID int 4 -99999999 meta.id

programmeID Programme SHARKSv20210222 UID of the archived programme coded as above int 4 meta.id

programmeID Programme SHARKSv20210421 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ULTRAVISTADR4 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSDR1 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSDR2 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSDR3 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSDR4 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSDR5 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSDR6 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20120926 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20130417 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20150108 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20160114 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20160507 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20170630 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20180419 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VHSv20201209 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIDEODR2 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIDEODR3 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIDEODR4 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIDEODR5 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIDEOv20100513 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIDEOv20111208 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGDR2 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGDR3 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGDR4 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20110714 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20111019 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20130417 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20150421 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20151230 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20160406 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20161202 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VIKINGv20170715 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCDEEPv20230713 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCDR1 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCDR3 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCDR4 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCDR5 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20110816 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20110909 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20120126 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20121128 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20130304 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20130805 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20140428 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20140903 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20150309 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20151218 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20160311 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20160822 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20170109 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20170411 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20171101 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20180702 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20181120 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20191212 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20210708 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20230816 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VMCv20240226 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VSAQC UID of the archived programme coded as above int 4 meta.id

programmeID Programme VVVDR1 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VVVDR2 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VVVDR5 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VVVXDR1 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VVVv20100531 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VVVv20110718 UID of the archived programme coded as above int 4 meta.id

programmeID SurveyProgrammes SHARKSv20210222 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes SHARKSv20210421 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ULTRAVISTADR4 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSDR1 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSDR2 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSDR3 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSDR4 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSDR5 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSDR6 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20120926 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20130417 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20150108 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20160114 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20160507 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20170630 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20180419 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VHSv20201209 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIDEODR2 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIDEODR3 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIDEODR4 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIDEODR5 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIDEOv20100513 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIDEOv20111208 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGDR2 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGDR3 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGDR4 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20110714 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20111019 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20130417 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20150421 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20151230 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20160406 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20161202 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VIKINGv20170715 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCDEEPv20230713 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCDR1 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCDR3 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCDR4 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCDR5 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20110816 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20110909 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20120126 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20121128 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20130304 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20130805 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20140428 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20140903 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20150309 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20151218 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20160311 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20160822 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20170109 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20170411 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20171101 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20180702 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20181120 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20191212 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20210708 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20230816 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VMCv20240226 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VSAQC VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VVVDR1 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VVVDR2 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VVVDR5 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VVVXDR1 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VVVv20100531 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VVVv20110718 VSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

project Multiframe SHARKSv20210222 Time-allocation code varchar 64 NONE meta.bib

project Multiframe SHARKSv20210421 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ULTRAVISTADR4 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSDR1 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSDR2 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSDR3 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSDR4 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSDR5 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSDR6 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20120926 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20130417 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20140409 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20150108 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20160114 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20160507 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20170630 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20180419 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VHSv20201209 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIDEODR2 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIDEODR3 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIDEODR4 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIDEODR5 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIDEOv20100513 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIDEOv20111208 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGDR2 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGDR3 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGDR4 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20110714 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20111019 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20130417 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20140402 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20150421 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20151230 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20160406 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20161202 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VIKINGv20170715 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCDEEPv20230713 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCDR1 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCDR2 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCDR3 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCDR4 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCDR5 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20110816 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20110909 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20120126 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20121128 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20130304 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20130805 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20140428 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20140903 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20150309 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20151218 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20160311 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20160822 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20170109 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20170411 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20171101 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20180702 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20181120 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20191212 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20210708 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20230816 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VMCv20240226 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VVVDR1 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VVVDR2 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VVVDR5 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VVVXDR1 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VVVv20100531 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VVVv20110718 Time-allocation code varchar 64 NONE meta.bib

project sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Time-allocation code varchar 64 NONE meta.bib

projectionID ObjectThin PS1DR2 Projection cell identifier. smallint 2 -1 meta.id

projectionID StackObjectThin PS1DR2 Projection cell identifier. smallint 2 -1

projp1 CurrentAstrometry SHARKSv20210222 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry SHARKSv20210421 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry ULTRAVISTADR4 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSDR1 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSDR2 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSDR3 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSDR4 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSDR5 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSDR6 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20120926 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20130417 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20140409 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20150108 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20160114 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20160507 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20170630 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20180419 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VHSv20201209 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIDEODR2 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIDEODR3 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIDEODR4 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIDEODR5 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIDEOv20100513 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIDEOv20111208 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGDR2 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGDR3 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGDR4 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20110714 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20111019 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20130417 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20140402 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20150421 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20151230 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20160406 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20161202 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VIKINGv20170715 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCDEEPv20230713 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCDR1 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCDR2 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCDR3 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCDR4 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCDR5 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20110816 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20110909 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20120126 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20121128 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20130304 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20130805 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20140428 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20140903 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20150309 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20151218 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20160311 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20160822 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20170109 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20170411 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20171101 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20180702 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20181120 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20191212 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20210708 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20230816 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VMCv20240226 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VVVDR1 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VVVDR2 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VVVDR5 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VVVXDR1 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VVVv20100531 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 CurrentAstrometry VVVv20110718 Old style WCS {image extension keyword: PROJP1} float 8 -9.999995e+08

projp1 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Old style WCS float 8 -9.999995e+08

projp3 CurrentAstrometry SHARKSv20210222 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry SHARKSv20210421 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry ULTRAVISTADR4 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSDR1 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSDR2 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSDR3 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSDR4 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSDR5 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSDR6 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSv20120926 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSv20130417 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

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projp3 CurrentAstrometry VHSv20150108 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VHSv20160114 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

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projp3 CurrentAstrometry VHSv20170630 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

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projp3 CurrentAstrometry VHSv20201209 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIDEODR2 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIDEODR3 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIDEODR4 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIDEODR5 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIDEOv20100513 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

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projp3 CurrentAstrometry VIKINGDR2 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGDR3 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGDR4 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20110714 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20111019 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20130417 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20140402 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20150421 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20151230 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20160406 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VIKINGv20161202 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

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projp3 CurrentAstrometry VMCDEEPv20230713 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCDR1 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCDR2 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

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projp3 CurrentAstrometry VMCDR4 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCDR5 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20110816 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

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projp3 CurrentAstrometry VMCv20120126 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20121128 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20130304 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20130805 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20140428 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20140903 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20150309 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20151218 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20160311 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20160822 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20170109 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20170411 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20171101 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20180702 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20181120 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20191212 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20210708 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20230816 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VMCv20240226 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VVVDR1 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VVVDR2 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VVVDR5 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VVVXDR1 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VVVv20100531 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 CurrentAstrometry VVVv20110718 Old style WCS {image extension keyword: PROJP3} float 8 -9.999995e+08

projp3 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Old style WCS float 8 -9.999995e+08

projp5 CurrentAstrometry SHARKSv20210222 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry SHARKSv20210421 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry ULTRAVISTADR4 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSDR1 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSDR2 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSDR3 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSDR4 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSDR5 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSDR6 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20120926 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20130417 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20140409 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20150108 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20160114 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20160507 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20170630 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20180419 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VHSv20201209 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIDEODR2 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIDEODR3 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIDEODR4 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIDEODR5 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIDEOv20100513 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIDEOv20111208 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGDR2 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGDR3 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGDR4 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20110714 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20111019 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20130417 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20140402 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20150421 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20151230 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20160406 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20161202 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VIKINGv20170715 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCDEEPv20230713 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCDR1 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCDR2 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCDR3 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCDR4 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCDR5 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20110816 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20110909 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20120126 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20121128 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20130304 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20130805 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20140428 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20140903 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20150309 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20151218 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20160311 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20160822 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20170109 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20170411 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20171101 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20180702 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20181120 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20191212 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20210708 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20230816 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VMCv20240226 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VVVDR1 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VVVDR2 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VVVDR5 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VVVXDR1 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VVVv20100531 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 CurrentAstrometry VVVv20110718 Old style WCS {image extension keyword: PROJP5} float 8 -9.999995e+08

projp5 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Old style WCS float 8 -9.999995e+08

propPeriod Programme SHARKSv20210222 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme SHARKSv20210421 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ULTRAVISTADR4 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSDR1 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSDR2 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSDR3 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSDR4 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSDR5 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSDR6 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20120926 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20130417 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20150108 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20160114 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20160507 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20170630 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20180419 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VHSv20201209 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIDEODR2 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIDEODR3 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIDEODR4 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIDEODR5 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIDEOv20100513 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIDEOv20111208 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGDR2 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGDR3 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGDR4 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20110714 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20111019 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20130417 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20150421 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20151230 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20160406 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20161202 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VIKINGv20170715 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCDEEPv20230713 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCDR1 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCDR3 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCDR4 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCDR5 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20110816 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20110909 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20120126 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20121128 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20130304 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20130805 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20140428 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20140903 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20150309 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20151218 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20160311 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20160822 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20170109 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20170411 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20171101 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20180702 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20181120 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20191212 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20210708 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20230816 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VMCv20240226 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VSAQC the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VVVDR1 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VVVDR2 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VVVDR5 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VVVXDR1 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VVVv20100531 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VVVv20110718 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

proprietary Survey SHARKSv20210222 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey SHARKSv20210421 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ULTRAVISTADR4 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSDR1 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSDR2 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSDR3 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSDR4 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSDR5 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSDR6 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20120926 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20130417 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20150108 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20160114 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20160507 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20170630 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20180419 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VHSv20201209 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIDEODR2 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIDEODR3 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIDEODR4 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIDEODR5 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIDEOv20100513 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIDEOv20111208 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGDR2 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGDR3 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGDR4 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20110714 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20111019 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20130417 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20150421 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20151230 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20160406 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20161202 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VIKINGv20170715 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCDEEPv20230713 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCDR1 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCDR3 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCDR4 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCDR5 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20110816 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20110909 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20120126 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20121128 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20130304 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20130805 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20140428 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20140903 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20150309 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20151218 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20160311 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20160822 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20170109 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20170411 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20171101 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20180702 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20181120 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20191212 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20210708 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20230816 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VMCv20240226 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VSAQC Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VVVDR1 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VVVDR2 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VVVDR5 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VVVXDR1 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VVVv20100531 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VVVv20110718 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

prox masterDR2 SKYMAPPER Distance to next-closest DR2 source (=15 if no sources within 15 arcsec) real 4 arcsec pos.angDistance

prox twomass_psc, twomass_xsc TWOMASS Proximity. real 4 arcsec pos.angDistance

prox tycho2 GAIADR1 Proximity indicator smallint 2 0.1 arcsec pos.angDistance

prox_id masterDR2 SKYMAPPER object_id of next-closest DR2 source bigint 8 meta.id

ps1_dr1_dist masterDR2 SKYMAPPER Distance on sky to closest Pan-STARRS1 DR1 source real 4 arcsec pos.angDistance

ps1_dr1_id masterDR2 SKYMAPPER Unique identifier (objID) of closest Pan-STARRS1 DR1 source bigint 8 meta.id.cross

pSaturated sharksSource SHARKSv20210222 Probability that the source is saturated 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.

pSaturated sharksSource SHARKSv20210421 Probability that the source is saturated 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.

pSaturated ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTADR4 Probability that the source is saturated 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.

pSaturated vhsSource VHSDR1 Probability that the source is saturated 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.

pSaturated vhsSource VHSDR2 Probability that the source is saturated 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.

pSaturated vhsSource VHSDR3 Probability that the source is saturated 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.

pSaturated vhsSource VHSDR4 Probability that the source is saturated 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.

pSaturated vhsSource VHSDR5 Probability that the source is saturated 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.

pSaturated vhsSource VHSDR6 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20120926 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20130417 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20140409 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20150108 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20160114 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20160507 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20170630 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20180419 Probability that the source is saturated 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.

pSaturated vhsSource VHSv20201209 Probability that the source is saturated 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.

pSaturated videoSource VIDEODR2 Probability that the source is saturated 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.

pSaturated videoSource VIDEODR3 Probability that the source is saturated 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.

pSaturated videoSource VIDEODR4 Probability that the source is saturated 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.

pSaturated videoSource VIDEODR5 Probability that the source is saturated 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.

pSaturated videoSource VIDEOv20100513 Probability that the source is saturated 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.

pSaturated videoSource VIDEOv20111208 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGDR2 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGDR3 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGDR4 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20110714 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20111019 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20130417 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20140402 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20150421 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20151230 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20160406 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20161202 Probability that the source is saturated 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.

pSaturated vikingSource VIKINGv20170715 Probability that the source is saturated 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.

pSaturated vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Probability that the source is saturated 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.

pSaturated vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Probability that the source is saturated 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.

pSaturated vmcSource VMCDR2 Probability that the source is saturated 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.

pSaturated vmcSource VMCDR3 Probability that the source is saturated 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.

pSaturated vmcSource VMCDR4 Probability that the source is saturated 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.

pSaturated vmcSource VMCDR5 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20110816 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20110909 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20120126 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20121128 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20130304 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20130805 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20140428 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20140903 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20150309 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20151218 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20160311 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20160822 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20170109 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20170411 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20171101 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20180702 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20181120 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20191212 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20210708 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20230816 Probability that the source is saturated 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.

pSaturated vmcSource VMCv20240226 Probability that the source is saturated 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.

pSaturated vmcSource, vmcSynopticSource VMCDR1 Probability that the source is saturated 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.

pSaturated vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Probability that the source is saturated 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.

pSaturated vvvSource VVVDR2 Probability that the source is saturated 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.

pSaturated vvvSource VVVDR5 Probability that the source is saturated 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.

pSaturated vvvSource VVVv20100531 Probability that the source is saturated 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.

pSaturated vvvSource VVVv20110718 Probability that the source is saturated 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.

pSaturated vvvSource, vvvSynopticSource VVVDR1 Probability that the source is saturated 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.

pseudocolour gaia_source GAIAEDR3 Astrometrically determined pseudocolour of the source real 4 micron^-1 em.wavenumber

pseudocolour_error gaia_source GAIAEDR3 STandard error of the pseudocolour of the source real 4 micron^-1 em.wavenumber

psfChiSq Detection PS1DR2 Reduced chi squared value of the PSF model fit. real 4 -999

psfCore Detection PS1DR2 PSF core parameter k, where F = F0 / (1 + k r^2 + r^3.33). real 4 -999

psfFitChi2 sharksDetection SHARKSv20210222 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 sharksDetection SHARKSv20210421 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSDR2 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSDR3 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSDR4 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSDR5 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSDR6 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20120926 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20130417 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20140409 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20150108 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20160114 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20160507 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20170630 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20180419 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection VHSv20201209 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vhsDetection, vhsListRemeasurement VHSDR1 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9

psfFitChi2 videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9

psfFitChi2 videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9

psfFitChi2 videoListRemeasurement VIDEOv20100513 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGDR2 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGDR3 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGDR4 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20111019 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20130417 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20140402 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20150421 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20151230 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20160406 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20161202 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection VIKINGv20170715 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vikingDetection, vikingListRemeasurement VIKINGv20110714 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCDR1 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCDR2 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCDR3 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCDR4 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCDR5 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20110909 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20120126 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20121128 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20130304 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20130805 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20140428 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20140903 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20150309 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20151218 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20160311 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20160822 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20170109 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20170411 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20171101 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20180702 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20181120 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20191212 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20210708 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20230816 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection VMCv20240226 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcDetection, vmcListRemeasurement VMCv20110816 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vmcdeepDetection VMCDEEPv20230713 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vvvDetection VVVDR1 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vvvDetection VVVDR2 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vvvDetection, vvvListRemeasurement VVVv20100531 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitDof sharksDetection SHARKSv20210222 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof sharksDetection SHARKSv20210421 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSDR2 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSDR3 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSDR4 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSDR5 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSDR6 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20120926 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20130417 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20140409 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20150108 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20160114 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20160507 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20170630 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20180419 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection VHSv20201209 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vhsDetection, vhsListRemeasurement VHSDR1 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999

psfFitDof videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999

psfFitDof videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999

psfFitDof videoListRemeasurement VIDEOv20100513 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGDR2 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGDR3 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGDR4 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20111019 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20130417 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20140402 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20150421 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20151230 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20160406 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20161202 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection VIKINGv20170715 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vikingDetection, vikingListRemeasurement VIKINGv20110714 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCDR1 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCDR2 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCDR3 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCDR4 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCDR5 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20110909 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20120126 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20121128 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20130304 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20130805 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20140428 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20140903 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20150309 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20151218 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20160311 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20160822 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20170109 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20170411 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20171101 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20180702 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20181120 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20191212 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20210708 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20230816 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection VMCv20240226 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcDetection, vmcListRemeasurement VMCv20110816 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vmcdeepDetection VMCDEEPv20230713 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vvvDetection VVVDR1 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vvvDetection VVVDR2 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vvvDetection, vvvListRemeasurement VVVv20100531 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitX sharksDetection SHARKSv20210222 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX sharksDetection SHARKSv20210421 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4 -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSDR2 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSDR3 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSDR4 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSDR5 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSDR6 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20120926 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20130417 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20140409 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20150108 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20160114 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20160507 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20170630 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20180419 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection VHSv20201209 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vhsDetection, vhsListRemeasurement VHSDR1 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4 -0.9999995e9

psfFitX videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4 -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4 -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4 -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4 -0.9999995e9

psfFitX videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4 -0.9999995e9

psfFitX videoListRemeasurement VIDEOv20100513 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGDR2 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGDR3 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGDR4 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20111019 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20130417 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20140402 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20150421 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20151230 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20160406 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20161202 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection VIKINGv20170715 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vikingDetection, vikingListRemeasurement VIKINGv20110714 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCDR1 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCDR2 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCDR3 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCDR4 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCDR5 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20110909 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20120126 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20121128 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20130304 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20130805 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20140428 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20140903 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20150309 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20151218 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20160311 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20160822 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20170109 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20170411 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20171101 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20180702 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20181120 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20191212 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20210708 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20230816 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection VMCv20240226 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcDetection, vmcListRemeasurement VMCv20110816 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vmcdeepDetection VMCDEEPv20230713 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vvvDetection VVVDR1 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vvvDetection VVVDR2 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vvvDetection, vvvListRemeasurement VVVv20100531 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitXerr sharksDetection SHARKSv20210222 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr sharksDetection SHARKSv20210421 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4 -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSDR2 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSDR3 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSDR4 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSDR5 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSDR6 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20120926 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20130417 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20140409 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20150108 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20160114 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20160507 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20170630 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20180419 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection VHSv20201209 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vhsDetection, vhsListRemeasurement VHSDR1 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4 -0.9999995e9

psfFitXerr videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4 -0.9999995e9 stat.error

psfFitXerr videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4 -0.9999995e9 stat.error

psfFitXerr videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4 -0.9999995e9 stat.error

psfFitXerr videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4 -0.9999995e9

psfFitXerr videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4 -0.9999995e9

psfFitXerr videoListRemeasurement VIDEOv20100513 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGDR2 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGDR3 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGDR4 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20111019 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20130417 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20140402 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20150421 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20151230 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20160406 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20161202 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection VIKINGv20170715 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vikingDetection, vikingListRemeasurement VIKINGv20110714 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCDR1 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCDR2 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCDR3 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCDR4 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCDR5 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20110909 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20120126 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20121128 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20130304 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20130805 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20140428 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20140903 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20150309 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20151218 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20160311 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20160822 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20170109 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20170411 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20171101 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20180702 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20181120 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20191212 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20210708 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20230816 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection VMCv20240226 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcDetection, vmcListRemeasurement VMCv20110816 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vmcdeepDetection VMCDEEPv20230713 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vvvDetection VVVDR1 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vvvDetection VVVDR2 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vvvDetection, vvvListRemeasurement VVVv20100531 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitY sharksDetection SHARKSv20210222 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY sharksDetection SHARKSv20210421 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4 -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSDR2 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSDR3 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSDR4 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSDR5 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSDR6 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20120926 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20130417 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20140409 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20150108 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20160114 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20160507 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20170630 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20180419 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection VHSv20201209 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vhsDetection, vhsListRemeasurement VHSDR1 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4 -0.9999995e9

psfFitY videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4 -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4 -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4 -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4 -0.9999995e9

psfFitY videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4 -0.9999995e9

psfFitY videoListRemeasurement VIDEOv20100513 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGDR2 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGDR3 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGDR4 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20111019 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20130417 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20140402 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20150421 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20151230 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20160406 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20161202 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection VIKINGv20170715 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vikingDetection, vikingListRemeasurement VIKINGv20110714 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCDR1 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCDR2 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCDR3 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCDR4 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCDR5 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20110909 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20120126 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20121128 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20130304 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20130805 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20140428 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20140903 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20150309 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20151218 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20160311 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20160822 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20170109 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20170411 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20171101 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20180702 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20181120 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20191212 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20210708 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20230816 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection VMCv20240226 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcDetection, vmcListRemeasurement VMCv20110816 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vmcdeepDetection VMCDEEPv20230713 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vvvDetection VVVDR1 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vvvDetection VVVDR2 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vvvDetection, vvvListRemeasurement VVVv20100531 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitYerr sharksDetection SHARKSv20210222 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr sharksDetection SHARKSv20210421 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4 -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSDR2 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSDR3 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSDR4 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSDR5 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSDR6 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20120926 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20130417 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20140409 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20150108 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20160114 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20160507 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20170630 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20180419 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection VHSv20201209 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vhsDetection, vhsListRemeasurement VHSDR1 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4 -0.9999995e9

psfFitYerr videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4 -0.9999995e9 stat.error

psfFitYerr videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4 -0.9999995e9 stat.error

psfFitYerr videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4 -0.9999995e9 stat.error

psfFitYerr videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4 -0.9999995e9

psfFitYerr videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4 -0.9999995e9

psfFitYerr videoListRemeasurement VIDEOv20100513 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGDR2 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGDR3 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGDR4 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20111019 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20130417 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20140402 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20150421 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20151230 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20160406 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20161202 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection VIKINGv20170715 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vikingDetection, vikingListRemeasurement VIKINGv20110714 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCDR1 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCDR2 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCDR3 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCDR4 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCDR5 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20110909 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20120126 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20121128 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20130304 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20130805 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20140428 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20140903 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20150309 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20151218 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20160311 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20160822 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20170109 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20170411 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20171101 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20180702 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20181120 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20191212 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20210708 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20230816 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection VMCv20240226 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcDetection, vmcListRemeasurement VMCv20110816 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vmcdeepDetection VMCDEEPv20230713 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vvvDetection VVVDR1 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vvvDetection VVVDR2 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vvvDetection, vvvListRemeasurement VVVv20100531 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFlux Detection PS1DR2 Flux from PSF fit. real 4 Janskys -999

psfFlux sharksDetection SHARKSv20210222 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux sharksDetection SHARKSv20210421 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_flux} real 4 -0.9999995e9 phot.count

psfFlux vhsDetection VHSDR2 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vhsDetection VHSDR3 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSDR4 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSDR5 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSDR6 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20120926 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20130417 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20140409 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20150108 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20160114 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20160507 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20170630 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20180419 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection VHSv20201209 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vhsDetection, vhsListRemeasurement VHSDR1 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_flux} real 4 -0.9999995e9

psfFlux videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_flux} real 4 -0.9999995e9 phot.count

psfFlux videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_flux} real 4 -0.9999995e9 phot.count

psfFlux videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_flux} real 4 -0.9999995e9 phot.count

psfFlux videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_flux} real 4 -0.9999995e9

psfFlux videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_flux} real 4 -0.9999995e9

psfFlux videoListRemeasurement VIDEOv20100513 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vikingDetection VIKINGDR2 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vikingDetection VIKINGDR3 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGDR4 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGv20111019 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vikingDetection VIKINGv20130417 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGv20140402 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGv20150421 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGv20151230 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGv20160406 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGv20161202 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection VIKINGv20170715 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vikingDetection, vikingListRemeasurement VIKINGv20110714 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vmcDetection VMCDR1 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vmcDetection VMCDR2 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCDR3 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCDR4 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCDR5 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20110909 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vmcDetection VMCv20120126 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vmcDetection VMCv20121128 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20130304 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20130805 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20140428 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20140903 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20150309 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20151218 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20160311 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20160822 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20170109 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20170411 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20171101 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20180702 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20181120 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20191212 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20210708 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20230816 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection VMCv20240226 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vmcDetection, vmcListRemeasurement VMCv20110816 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vmcdeepDetection VMCDEEPv20230713 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vvvDetection VVVDR1 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vvvDetection VVVDR2 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count

psfFlux vvvDetection, vvvListRemeasurement VVVv20100531 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFluxErr Detection PS1DR2 Error on flux from PSF fit. real 4 Janskys -999

psfFluxErr sharksDetection SHARKSv20210222 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr sharksDetection SHARKSv20210421 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr ultravistaDetection ULTRAVISTADR4 Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4 -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSDR2 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSDR3 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSDR4 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSDR5 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSDR6 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20120926 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20130417 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20140409 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20150108 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20160114 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20160507 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20170630 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20180419 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection VHSv20201209 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vhsDetection, vhsListRemeasurement VHSDR1 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr videoDetection VIDEODR2 Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4 -0.9999995e9

psfFluxErr videoDetection VIDEODR3 Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4 -0.9999995e9 stat.error

psfFluxErr videoDetection VIDEODR4 Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4 -0.9999995e9 stat.error

psfFluxErr videoDetection VIDEODR5 Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4 -0.9999995e9 stat.error

psfFluxErr videoDetection VIDEOv20100513 Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4 -0.9999995e9

psfFluxErr videoDetection VIDEOv20111208 Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4 -0.9999995e9

psfFluxErr videoListRemeasurement VIDEOv20100513 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGDR2 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGDR3 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGDR4 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20111019 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20130417 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20140402 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20150421 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20151230 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20160406 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20161202 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection VIKINGv20170715 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vikingDetection, vikingListRemeasurement VIKINGv20110714 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCDR1 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCDR2 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCDR3 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCDR4 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCDR5 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20110909 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20120126 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20121128 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20130304 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20130805 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20140428 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20140903 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20150309 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20151218 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20160311 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20160822 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20170109 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20170411 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20171101 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20180702 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20181120 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20191212 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20210708 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20230816 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection VMCv20240226 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcDetection, vmcListRemeasurement VMCv20110816 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vmcdeepDetection VMCDEEPv20230713 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vvvDetection VVVDR1 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vvvDetection VVVDR2 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vvvDetection, vvvListRemeasurement VVVv20100531 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfID vmcPsfCatalogue VMCDR3 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCDR4 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20121128 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20140428 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20140903 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20150309 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20151218 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20160311 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20160822 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20170109 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20170411 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfCatalogue VMCv20171101 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfDetections VMCv20180702 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vmcPsfDetections VMCv20181120 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfID vvvPsfDaophotJKsSource, vvvPsfDophotZYJHKsSource VVVDR5 UID (unique over entire VSA via programme ID prefix) of this merged detection as assigned by merge algorithm bigint 8 meta.id;meta.main

psfLikelihood Detection PS1DR2 Likelihood that this detection is best fit by a PSF. real 4 -999

psfMag sharksDetection SHARKSv20210222 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag sharksDetection SHARKSv20210421 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag ultravistaDetection ULTRAVISTADR4 Not available in SE output real 4 -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSDR2 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vhsDetection VHSDR3 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vhsDetection VHSDR4 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSDR5 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSDR6 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSv20120926 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vhsDetection VHSv20130417 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vhsDetection VHSv20140409 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vhsDetection VHSv20150108 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSv20160114 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSv20160507 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSv20170630 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSv20180419 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection VHSv20201209 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vhsDetection, vhsListRemeasurement VHSDR1 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag videoDetection VIDEODR2 Not available in SE output real 4 -0.9999995e9

psfMag videoDetection VIDEODR3 Not available in SE output real 4 -0.9999995e9 stat.fit.param

psfMag videoDetection VIDEODR4 Not available in SE output real 4 -0.9999995e9 stat.fit.param;phot.mag

psfMag videoDetection VIDEODR5 Not available in SE output real 4 -0.9999995e9 stat.fit.param;phot.mag

psfMag videoDetection VIDEOv20100513 Not available in SE output real 4 -0.9999995e9

psfMag videoDetection VIDEOv20111208 Not available in SE output real 4 -0.9999995e9

psfMag videoListRemeasurement VIDEOv20100513 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vikingDetection VIKINGDR2 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vikingDetection VIKINGDR3 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vikingDetection VIKINGDR4 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vikingDetection VIKINGv20111019 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vikingDetection VIKINGv20130417 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vikingDetection VIKINGv20140402 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vikingDetection VIKINGv20150421 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vikingDetection VIKINGv20151230 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vikingDetection VIKINGv20160406 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vikingDetection VIKINGv20161202 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vikingDetection VIKINGv20170715 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vikingDetection, vikingListRemeasurement VIKINGv20110714 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vmcDetection VMCDR1 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vmcDetection VMCDR2 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vmcDetection VMCDR3 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCDR4 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCDR5 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20110909 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vmcDetection VMCv20120126 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vmcDetection VMCv20121128 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vmcDetection VMCv20130304 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vmcDetection VMCv20130805 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vmcDetection VMCv20140428 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vmcDetection VMCv20140903 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20150309 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20151218 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20160311 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20160822 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20170109 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20170411 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20171101 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20180702 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20181120 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20191212 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20210708 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20230816 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection VMCv20240226 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vmcDetection, vmcListRemeasurement VMCv20110816 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vmcdeepDetection VMCDEEPv20230713 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vvvDetection VVVDR1 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vvvDetection VVVDR2 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param

psfMag vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.fit.param;phot.mag

psfMag vvvDetection, vvvListRemeasurement VVVv20100531 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMagErr sharksDetection SHARKSv20210222 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr sharksDetection SHARKSv20210421 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr ultravistaDetection ULTRAVISTADR4 Not available in SE output real 4 -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSDR2 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vhsDetection VHSDR3 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vhsDetection VHSDR4 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSDR5 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSDR6 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSv20120926 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vhsDetection VHSv20130417 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vhsDetection VHSv20140409 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vhsDetection VHSv20150108 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSv20160114 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSv20160507 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSv20170630 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSv20180419 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection VHSv20201209 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vhsDetection, vhsListRemeasurement VHSDR1 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr videoDetection VIDEODR2 Not available in SE output real 4 -0.9999995e9

psfMagErr videoDetection VIDEODR3 Not available in SE output real 4 -0.9999995e9 stat.error

psfMagErr videoDetection VIDEODR4 Not available in SE output real 4 -0.9999995e9 stat.error;phot.mag

psfMagErr videoDetection VIDEODR5 Not available in SE output real 4 -0.9999995e9 stat.error;phot.mag

psfMagErr videoDetection VIDEOv20100513 Not available in SE output real 4 -0.9999995e9

psfMagErr videoDetection VIDEOv20111208 Not available in SE output real 4 -0.9999995e9

psfMagErr videoListRemeasurement VIDEOv20100513 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vikingDetection VIKINGDR2 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vikingDetection VIKINGDR3 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vikingDetection VIKINGDR4 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vikingDetection VIKINGv20111019 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vikingDetection VIKINGv20130417 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vikingDetection VIKINGv20140402 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vikingDetection VIKINGv20150421 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vikingDetection VIKINGv20151230 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vikingDetection VIKINGv20160406 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vikingDetection VIKINGv20161202 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vikingDetection VIKINGv20170715 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vikingDetection, vikingListRemeasurement VIKINGv20110714 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCDR1 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCDR2 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCDR3 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCDR4 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCDR5 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20110909 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCv20120126 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCv20121128 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCv20130304 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCv20130805 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCv20140428 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcDetection VMCv20140903 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20150309 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20151218 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20160311 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20160822 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20170109 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20170411 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20171101 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20180702 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20181120 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20191212 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20210708 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20230816 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection VMCv20240226 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vmcDetection, vmcListRemeasurement VMCv20110816 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vmcdeepDetection VMCDEEPv20230713 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vvvDetection VVVDR1 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vvvDetection VVVDR2 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles VVVDR5 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error;phot.mag

psfMagErr vvvDetection, vvvListRemeasurement VVVv20100531 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMajorFWHM Detection PS1DR2 PSF major axis FWHM. real 4 arcsec -999

psfMinorFWHM Detection PS1DR2 PSF minor axis FWHM. real 4 arcsec -999

psfQf Detection PS1DR2 PSF coverage factor. real 4 -999

psfQfPerfect Detection PS1DR2 PSF weighted fraction of pixels totally unmasked. real 4 -999

psfSourceID vmcPsfSource VMCDR5 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfSourceID vmcPsfSource VMCv20180702 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfSourceID vmcPsfSource VMCv20181120 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfSourceID vmcPsfSource VMCv20191212 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfSourceID vmcPsfSource VMCv20210708 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfSourceID vmcPsfSource VMCv20230816 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfSourceID vmcPsfSource VMCv20240226 UID of VMC PSF extracted objects bigint 8 meta.id;meta.main

psfTheta Detection PS1DR2 PSF major axis orientation. real 4 degrees -999

pStar sharksSource SHARKSv20210222 Probability that the source is a star 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.

pStar sharksSource SHARKSv20210421 Probability that the source is a star 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.

pStar ultravistaSource, ultravistaSourceRemeasurement ULTRAVISTADR4 Probability that the source is a star 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.

pStar vhsSource VHSDR1 Probability that the source is a star 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.

pStar vhsSource VHSDR2 Probability that the source is a star 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.

pStar vhsSource VHSDR3 Probability that the source is a star 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.

pStar vhsSource VHSDR4 Probability that the source is a star 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.

pStar vhsSource VHSDR5 Probability that the source is a star 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.

pStar vhsSource VHSDR6 Probability that the source is a star 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.

pStar vhsSource VHSv20120926 Probability that the source is a star 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.

pStar vhsSource VHSv20130417 Probability that the source is a star 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.

pStar vhsSource VHSv20140409 Probability that the source is a star 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.

pStar vhsSource VHSv20150108 Probability that the source is a star 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.

pStar vhsSource VHSv20160114 Probability that the source is a star 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.

pStar vhsSource VHSv20160507 Probability that the source is a star 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.

pStar vhsSource VHSv20170630 Probability that the source is a star 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.

pStar vhsSource VHSv20180419 Probability that the source is a star 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.

pStar vhsSource VHSv20201209 Probability that the source is a star 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.

pStar videoSource VIDEODR2 Probability that the source is a star 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.

pStar videoSource VIDEODR3 Probability that the source is a star 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.

pStar videoSource VIDEODR4 Probability that the source is a star 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.

pStar videoSource VIDEODR5 Probability that the source is a star 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.

pStar videoSource VIDEOv20100513 Probability that the source is a star 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.

pStar videoSource VIDEOv20111208 Probability that the source is a star 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.

pStar vikingSource VIKINGDR2 Probability that the source is a star 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.

pStar vikingSource VIKINGDR3 Probability that the source is a star 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.

pStar vikingSource VIKINGDR4 Probability that the source is a star 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.

pStar vikingSource VIKINGv20110714 Probability that the source is a star 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.

pStar vikingSource VIKINGv20111019 Probability that the source is a star 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.

pStar vikingSource VIKINGv20130417 Probability that the source is a star 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.

pStar vikingSource VIKINGv20140402 Probability that the source is a star 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.

pStar vikingSource VIKINGv20150421 Probability that the source is a star 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.

pStar vikingSource VIKINGv20151230 Probability that the source is a star 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.

pStar vikingSource VIKINGv20160406 Probability that the source is a star 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.

pStar vikingSource VIKINGv20161202 Probability that the source is a star 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.

pStar vikingSource VIKINGv20170715 Probability that the source is a star 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.

pStar vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20160909 Probability that the source is a star 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.

pStar vikingZY_selJ_SourceRemeasurement VIKINGZYSELJv20170124 Probability that the source is a star 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.

pStar vmcSource VMCDR2 Probability that the source is a star 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.

pStar vmcSource VMCDR3 Probability that the source is a star 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.

pStar vmcSource VMCDR4 Probability that the source is a star 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.

pStar vmcSource VMCDR5 Probability that the source is a star 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.

pStar vmcSource VMCv20110816 Probability that the source is a star 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.

pStar vmcSource VMCv20110909 Probability that the source is a star 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.

pStar vmcSource VMCv20120126 Probability that the source is a star 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.

pStar vmcSource VMCv20121128 Probability that the source is a star 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.

pStar vmcSource VMCv20130304 Probability that the source is a star 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.

pStar vmcSource VMCv20130805 Probability that the source is a star 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.

pStar vmcSource VMCv20140428 Probability that the source is a star 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.

pStar vmcSource VMCv20140903 Probability that the source is a star 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.

pStar vmcSource VMCv20150309 Probability that the source is a star 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.

pStar vmcSource VMCv20151218 Probability that the source is a star 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.

pStar vmcSource VMCv20160311 Probability that the source is a star 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.

pStar vmcSource VMCv20160822 Probability that the source is a star 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.

pStar vmcSource VMCv20170109 Probability that the source is a star 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.

pStar vmcSource VMCv20170411 Probability that the source is a star 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.

pStar vmcSource VMCv20171101 Probability that the source is a star 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.

pStar vmcSource VMCv20180702 Probability that the source is a star 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.

pStar vmcSource VMCv20181120 Probability that the source is a star 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.

pStar vmcSource VMCv20191212 Probability that the source is a star 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.

pStar vmcSource VMCv20210708 Probability that the source is a star 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.

pStar vmcSource VMCv20230816 Probability that the source is a star 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.

pStar vmcSource VMCv20240226 Probability that the source is a star 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.

pStar vmcSource, vmcSynopticSource VMCDR1 Probability that the source is a star 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.

pStar vmcdeepSource, vmcdeepSynopticSource VMCDEEPv20230713 Probability that the source is a star 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.

pStar vvvSource VVVDR2 Probability that the source is a star 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.

pStar vvvSource VVVDR5 Probability that the source is a star 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.

pStar vvvSource VVVv20100531 Probability that the source is a star 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.

pStar vvvSource VVVv20110718 Probability that the source is a star 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.

pStar vvvSource, vvvSynopticSource VVVDR1 Probability that the source is a star 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.

pts_key twomass_psc TWOMASS A unique identification number for the PSC source. int 4 meta.id

pts_key twomass_xsc TWOMASS key to point source data DB record. int 4 meta.id

publicDb Release SHARKSv20210222 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release SHARKSv20210421 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VHSv20201209 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VMCDEEPv20230713 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VMCDR5 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VMCv20191212 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VMCv20210708 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VMCv20230816 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VMCv20240226 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VVVDR5 the name of the SQL Server database containing the public release varchar 128 NONE ??

publicDb Release VVVXDR1 the name of the SQL Server database containing the public release varchar 128 NONE ??

pv21 CurrentAstrometry SHARKSv20210222 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry SHARKSv20210421 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry ULTRAVISTADR4 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VHSDR1 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSDR2 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSDR3 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSDR4 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSDR5 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VHSDR6 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VHSv20120926 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSv20130417 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSv20140409 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSv20150108 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VHSv20160114 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VHSv20160507 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VHSv20170630 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VHSv20180419 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VHSv20201209 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VIDEODR2 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIDEODR3 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIDEODR4 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIDEODR5 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VIDEOv20100513 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIDEOv20111208 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGDR2 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGDR3 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGDR4 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGv20110714 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGv20111019 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGv20130417 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGv20140402 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VIKINGv20150421 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9

pv21 CurrentAstrometry VIKINGv20151230 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VIKINGv20160406 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VIKINGv20161202 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VIKINGv20170715 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCDEEPv20230713 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCDR1 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCDR2 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCDR3 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCDR4 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCDR5 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20110816 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20110909 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20120126 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20121128 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20130304 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20130805 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20140428 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20140903 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20150309 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VMCv20151218 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20160311 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20160822 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20170109 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20170411 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20171101 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20180702 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20181120 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20191212 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20210708 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20230816 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VMCv20240226 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VVVDR1 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VVVDR2 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VVVDR5 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VVVXDR1 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv21 CurrentAstrometry VVVv20100531 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 CurrentAstrometry VVVv20110718 Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv21 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Coefficient for r term (use only with ZPN projection) float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry SHARKSv20210222 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry SHARKSv20210421 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry ULTRAVISTADR4 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VHSDR1 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSDR2 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSDR3 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSDR4 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSDR5 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VHSDR6 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VHSv20120926 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSv20130417 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSv20140409 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSv20150108 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VHSv20160114 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VHSv20160507 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VHSv20170630 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VHSv20180419 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VHSv20201209 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VIDEODR2 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIDEODR3 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIDEODR4 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIDEODR5 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VIDEOv20100513 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIDEOv20111208 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGDR2 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGDR3 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGDR4 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGv20110714 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGv20111019 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGv20130417 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGv20140402 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VIKINGv20150421 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9

pv22 CurrentAstrometry VIKINGv20151230 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VIKINGv20160406 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VIKINGv20161202 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VIKINGv20170715 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCDEEPv20230713 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCDR1 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCDR2 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCDR3 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCDR4 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCDR5 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20110816 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20110909 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20120126 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20121128 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20130304 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20130805 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20140428 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20140903 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20150309 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VMCv20151218 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20160311 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20160822 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20170109 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20170411 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20171101 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20180702 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20181120 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20191212 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20210708 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20230816 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VMCv20240226 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VVVDR1 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VVVDR2 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VVVDR5 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VVVXDR1 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv22 CurrentAstrometry VVVv20100531 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 CurrentAstrometry VVVv20110718 Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv22 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Coefficient for r**2 term (use only with ZPN projection) float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry SHARKSv20210222 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry SHARKSv20210421 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry ULTRAVISTADR4 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VHSDR1 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSDR2 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSDR3 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSDR4 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSDR5 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VHSDR6 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VHSv20120926 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSv20130417 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSv20140409 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSv20150108 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VHSv20160114 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VHSv20160507 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VHSv20170630 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VHSv20180419 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VHSv20201209 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VIDEODR2 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIDEODR3 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIDEODR4 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIDEODR5 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VIDEOv20100513 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIDEOv20111208 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGDR2 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGDR3 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGDR4 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGv20110714 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGv20111019 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGv20130417 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGv20140402 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VIKINGv20150421 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9

pv23 CurrentAstrometry VIKINGv20151230 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VIKINGv20160406 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VIKINGv20161202 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VIKINGv20170715 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCDEEPv20230713 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCDR1 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCDR2 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCDR3 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCDR4 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCDR5 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20110816 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20110909 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20120126 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20121128 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20130304 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20130805 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20140428 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20140903 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20150309 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VMCv20151218 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20160311 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20160822 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20170109 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20170411 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20171101 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20180702 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20181120 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20191212 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20210708 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20230816 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VMCv20240226 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VVVDR1 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VVVDR2 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VVVDR5 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VVVXDR1 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv23 CurrentAstrometry VVVv20100531 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 CurrentAstrometry VVVv20110718 Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv23 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Coefficient for r**3 term (use only with ZPN projection) float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry SHARKSv20210222 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry SHARKSv20210421 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry ULTRAVISTADR4 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VHSDR1 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSDR2 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSDR3 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSDR4 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSDR5 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VHSDR6 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VHSv20120926 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSv20130417 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSv20140409 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSv20150108 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VHSv20160114 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VHSv20160507 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VHSv20170630 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VHSv20180419 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VHSv20201209 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VIDEODR2 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIDEODR3 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIDEODR4 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIDEODR5 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VIDEOv20100513 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIDEOv20111208 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGDR2 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGDR3 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGDR4 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGv20110714 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGv20111019 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGv20130417 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGv20140402 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VIKINGv20150421 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9

pv24 CurrentAstrometry VIKINGv20151230 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VIKINGv20160406 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VIKINGv20161202 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VIKINGv20170715 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCDEEPv20230713 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCDR1 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCDR2 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCDR3 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCDR4 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCDR5 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20110816 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20110909 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20120126 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20121128 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20130304 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20130805 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20140428 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20140903 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20150309 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VMCv20151218 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20160311 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20160822 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20170109 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20170411 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20171101 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20180702 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20181120 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20191212 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20210708 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20230816 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VMCv20240226 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VVVDR1 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VVVDR2 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VVVDR5 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VVVXDR1 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv24 CurrentAstrometry VVVv20100531 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 CurrentAstrometry VVVv20110718 Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv24 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Coefficient for r**4 term (use only with ZPN projection) float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry SHARKSv20210222 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry SHARKSv20210421 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry ULTRAVISTADR4 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VHSDR1 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSDR2 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSDR3 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSDR4 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSDR5 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VHSDR6 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VHSv20120926 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSv20130417 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSv20140409 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSv20150108 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VHSv20160114 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VHSv20160507 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VHSv20170630 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VHSv20180419 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VHSv20201209 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VIDEODR2 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIDEODR3 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIDEODR4 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIDEODR5 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VIDEOv20100513 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIDEOv20111208 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGDR2 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGDR3 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGDR4 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGv20110714 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGv20111019 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGv20130417 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGv20140402 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VIKINGv20150421 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9

pv25 CurrentAstrometry VIKINGv20151230 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VIKINGv20160406 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VIKINGv20161202 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VIKINGv20170715 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCDEEPv20230713 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCDR1 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCDR2 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCDR3 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCDR4 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCDR5 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20110816 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20110909 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20120126 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20121128 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20130304 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20130805 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20140428 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20140903 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20150309 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VMCv20151218 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20160311 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20160822 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20170109 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20170411 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20171101 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20180702 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20181120 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20191212 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20210708 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20230816 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VMCv20240226 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VVVDR1 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VVVDR2 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VVVDR5 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VVVXDR1 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 stat.fit.param

pv25 CurrentAstrometry VVVv20100531 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 CurrentAstrometry VVVv20110718 Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5}
transformation from pixel to celestial co-ordinates float 8 -0.9999995e9 pos.wcs.cdmatrix

pv25 sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC Coefficient for r**5 term (use only with ZPN projection) float 8 -0.9999995e9 pos.wcs.cdmatrix

pxcntr twomass_psc TWOMASS The pts_key value of the nearest source in the PSC. int 4 meta.number

pxcntr twomass_xsc TWOMASS ext_key value of nearest XSC source. int 4 meta.number

pxpa twomass_psc, twomass_xsc TWOMASS The position angle on the sky of the vector from the source to the nearest neighbor in the PSC, in degrees East of North. smallint 2 degrees pos.posAng

WFAU, Institute for Astronomy,
Royal Observatory, Blackford Hill
Edinburgh, EH9 3HJ, UK

vsa-support@roe.ac.uk
04/03/2024