P 
Name  Schema Table  Database  Description  Type  Length  Unit  Default Value  Unified Content Descriptor 
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 
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 
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, 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 
VIKINGDR5 
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 
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, 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 
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 
VVVDR4 
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 
VVVDR5 
ellipse fit orientation to 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 
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_2mass 
allwise_sc2 
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 


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 
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 
VIKINGDR5 
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 
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 
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 
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 
VVVDR4 
The pairing criterion for associating detections into merged sources 
real 
4 
Degrees 

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

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

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

?? 
parallax 
gaia_source 
GAIADR2 
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 
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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 
VIKINGDR5 
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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 
VVVDR4 
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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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 zaxis 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 chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic 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, 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_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, 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, tgas_source 
GAIADR1 
Correlation between parallax and proper motion in Right Ascension 
real 
4 


stat.correlation 
parallax_TGAS 
ravedr5Source 
RAVE 
Parallax 
float 
8 
mas 

pos.parallax 
paramTemplate 
RequiredMosaicTopLevel 
ULTRAVISTADR4 
Template file for SWARP parameters 
varchar 
32 



paramTemplate 
RequiredMosaicTopLevel 
VVVDR5 
Template file for SWARP parameters 
varchar 
32 



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



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



patternString 
Multiframe 
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 
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 
VIKINGDR5 
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 
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 
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 
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 
VVVDR4 
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} 
varchar 
64 

NONE 

patternString 
Multiframe 
VVVDR5 
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} 
varchar 
64 

NONE 

patternString 
Multiframe 
VVVv20110718 
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} 
varchar 
64 

NONE 

patternString 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
SADT pattern ID 
varchar 
64 

NONE 

pawprintdets 
vvvParallaxCatalogue 
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 

pawprintdets 
vvvParallaxCatalogue, vvvProperMotionCatalogue 
VVVDR4 
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 
Peaktopeak 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 peaktopeak 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 
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 
vmcRRlyraeVariables 
VMCDR4 
Period from OGLE3 survey {catalogue TType keyword: PERIOD} 
real 
4 
day 

time.period 
period 
vmcRRlyraeVariables 
VMCv20160822 
Period from OGLE3 survey {catalogue TType keyword: PERIOD} 
real 
4 
day 

time.period 
period 
vmcRRlyraeVariables 
VMCv20170109 
Period from OGLE3 survey {catalogue TType keyword: PERIOD} 
real 
4 
day 

time.period 
period 
vmcRRlyraeVariables 
VMCv20170411 
Period from OGLE3 survey {catalogue TType keyword: PERIOD} 
real 
4 
day 

time.period 
period 
vmcRRlyraeVariables 
VMCv20171101 
Period from OGLE3 survey {catalogue TType keyword: PERIOD} 
real 
4 
day 

time.period 
period 
vmcRRlyraeVariables 
VMCv20180702 
Period from OGLE3 survey {catalogue TType keyword: PERIOD} 
real 
4 
day 

time.period 
period 
vmcRRlyraeVariables 
VMCv20181120 
Period from OGLE3 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 
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, 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 
VIKINGDR5 
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 
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, 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 
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 
VVVDR4 
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} 
real 
4 
ADU 

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

phot.count 
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, 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 
VIKINGDR5 
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 
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, vmcListRemeasurement 
VMCv20110816 
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 
VVVDR4 
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} 
real 
4 
ADU 

stat.error 
petroFluxErr 
vvvDetection 
VVVDR5 
error on Petrosian flux 
real 
4 
ADU 

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

stat.error 
petroFluxErr 
vvvDetectionPawPrints, vvvDetectionTiles 
VVVDR5 
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 
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, 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 
VIKINGDR5 
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 
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, vmcListRemeasurement 
VMCv20110816 
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 
VVVDR4 
Calibrated Petrosian magnitude within circular aperture r_p 
real 
4 
mag 

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

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, 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 
VIKINGDR5 
error on calibrated Petrosian magnitude 
real 
4 
mag 

stat.error;phot.mag 
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 
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, vmcListRemeasurement 
VMCv20110816 
error on calibrated Petrosian magnitude 
real 
4 
mag 

stat.error 
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 
VVVDR4 
error on calibrated Petrosian magnitude 
real 
4 
mag 

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

stat.error;phot.mag 
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, 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 
VIKINGDR5 
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 
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, 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 
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 
VVVDR4 
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} 
real 
4 
pixels 

phys.angSize 
petroRad 
vvvDetection 
VVVDR5 
r_p as defined in Yasuda et al. 2001 AJ 112 1104 
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 
petroRad 
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 
PF_DEC 
mgcBrightSpec 
MGC 
PFr object declination in deg (J2000) 
float 
8 



PF_JMK 
mgcBrightSpec 
MGC 
PFr JK 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 
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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 
VIKINGDR5 
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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 
VVVDR4 
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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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_sc2 
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 profilefit photometry measurement in each band, as derived from the measurement signaltonoise ratio. 
varchar 
4 



 A  Source is detected in this band with a flux signaltonoise ratio w?snr>10.
 B  Source is detected in this band with a flux signaltonoise ratio 3<w?snr<10.
 C  Source is detected in this band with a flux signaltonoise ratio 2<w?snr<3.
 U  Upper limit on magnitude. Source measurement has w?snr<2. The profilefit magnitude w?mpro is a 95% confidence upper limit.
 X  A profilefit 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 profilefit 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 nonnull 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 3Band Cryo singleexposures.

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_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 
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, 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 
VIKINGDR5 
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 
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, 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 
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 
VVVDR4 
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 
VVVDR5 
Highest pixel value above sky 
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 
pHeight 
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 
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, 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 
VIKINGDR5 
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 
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, vmcListRemeasurement 
VMCv20110816 
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 
VVVDR4 
Error in peak height {catalogue TType keyword: Peak_height_err} 
real 
4 
ADU 

stat.error 
pHeightErr 
vvvDetection 
VVVDR5 
Error in peak height 
real 
4 
ADU 

stat.error 
pHeightErr 
vvvDetection, vvvListRemeasurement 
VVVv20100531 
Error in peak height {catalogue TType keyword: Peak_height_err} 
real 
4 
ADU 

stat.error 
pHeightErr 
vvvDetectionPawPrints, vvvDetectionTiles 
VVVDR5 
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_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_over_error 
gaia_source 
GAIADR2 
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_n_obs 
gaia_source 
GAIADR2 
Number of observations contributing to BP photometry 
int 
4 


meta.number 
phot_bp_rp_excess_factor 
gaia_source 
GAIADR2 
Combined BP and RP excess factor 
real 
4 



phot_flag 
combo17CDFSSource 
COMBO17 
flags on photometry: bit 07 (corresponding to values 0128) are original SExtractor flags, bit 911 set by COMBO17 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 
Gband mean flux 
float 
8 
electrons/s 

phot.flux;stat.mean;em.opt 
phot_g_mean_flux 
gaia_source, tgas_source 
GAIADR1 
Gband mean flux 
float 
8 
electrons/s 

phot.flux;stat.mean;em.opt 
phot_g_mean_flux_error 
gaia_source 
GAIADR2 
Error on Gband mean flux 
float 
8 
electrons/s 

stat.error;phot.flux;stat.mean;em.opt 
phot_g_mean_flux_error 
gaia_source, tgas_source 
GAIADR1 
Error on Gband mean flux 
float 
8 
electrons/s 

stat.error;phot.flux;stat.mean;em.opt 
phot_g_mean_flux_error_TGAS 
ravedr5Source 
RAVE 
Error on Gband 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 
Gband mean flux divided by its standard error 
float 
8 


arith.ratio 
phot_g_mean_flux_TGAS 
ravedr5Source 
RAVE 
Error on Gband 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 
Gband mean magnitude 
float 
8 
mag 

phot.mag;stat.mean;em.opt 
phot_g_mean_mag 
gaia_source 
GAIADR2 
Gband mean magnitude 
real 
4 
mag 

phot.mag;stat.mean;em.opt 
phot_g_mean_mag_TGAS 
ravedr5Source 
RAVE 
Gband 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, 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_rp_mean_flux 
gaia_source 
GAIADR2 
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_over_error 
gaia_source 
GAIADR2 
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_n_obs 
gaia_source 
GAIADR2 
Number of observations contributing to RP photometry 
int 
4 


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 
ULTRAVISTADR4 
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} 
real 
4 
mags 
0.9999995e9 
?? 
Derived detector zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued aperture correction. 
photZPCat 
MultiframeDetector 
VIKINGDR5 
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} 
real 
4 
mags 
0.9999995e9 
?? 
Derived detector zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued aperture correction. 
photZPCat 
MultiframeDetector 
VVVDR4 
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} 
real 
4 
mags 
0.9999995e9 
?? 
Derived detector zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued 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 zeropoint 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*log_{10}(flux/exptime)  aperCor  skyCorr Note that for the socalled total and isophotal flux options it is not possible to have a singlevalued aperture correction. 
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 
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 
VIKINGDR5 
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 
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 
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 
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 
VVVDR4 
Photometric zeropoint for default extinction in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPCat 
PreviousMFDZP 
VVVDR5 
Photometric zeropoint for default extinction in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPCat 
PreviousMFDZP 
VVVv20100531 
Photometric zeropoint for default extinction in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPCat 
PreviousMFDZP 
VVVv20110718 
Photometric zeropoint for default extinction in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPCat 
ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector 
VSAQC 
Photometric zero point for default extinction for the catalogue data 
real 
4 
mags 
0.9999995e9 
?? 
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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric night and worse. 
photZPErrCat 
MultiframeDetector 
VIKINGDR5 
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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric night and worse. 
photZPErrCat 
MultiframeDetector 
VVVDR4 
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 nonphotometric 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 nonphotometric 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 nonphotometric 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 nonphotometric night and worse. 
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 
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 
VIKINGDR5 
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 
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 
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 
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 
VVVDR4 
Photometric zeropoint error in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPErrCat 
PreviousMFDZP 
VVVDR5 
Photometric zeropoint error in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPErrCat 
PreviousMFDZP 
VVVv20100531 
Photometric zeropoint error in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPErrCat 
PreviousMFDZP 
VVVv20110718 
Photometric zeropoint error in catalogue header 
real 
4 
mag 
0.9999995e9 

photZPErrCat 
ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector 
VSAQC 
Photometric zero point error for the catalogue data 
real 
4 
mags 
0.9999995e9 
?? 
picoi 
Multiframe 
ULTRAVISTADR4 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSDR1 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSDR2 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSDR3 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSDR4 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSDR5 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSDR6 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20120926 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20130417 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20140409 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20150108 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20160114 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20160507 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20170630 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VHSv20180419 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIDEODR2 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIDEODR3 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIDEODR4 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIDEODR5 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIDEOv20100513 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIDEOv20111208 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGDR2 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGDR3 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGDR4 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGDR5 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20110714 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20111019 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20130417 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20140402 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20150421 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20151230 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20160406 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20161202 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VIKINGv20170715 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCDR1 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCDR2 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCDR3 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCDR4 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20110816 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20110909 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20120126 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20121128 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20130304 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20130805 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20140428 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20140903 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20150309 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20151218 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20160311 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20160822 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20170109 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20170411 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20171101 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20180702 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VMCv20181120 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VVVDR1 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VVVDR2 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VVVDR4 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VVVDR5 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VVVv20100531 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
Multiframe 
VVVv20110718 
PICOI name. {image primary HDU keyword: PICOI} 
varchar 
64 

NONE 

picoi 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
PICOI 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 
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 
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 
VIKINGDR5 
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 
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 
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 
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 
VVVDR4 
The final pixel size of the mosaic 
real 
4 
arcsec 

?? 
pixelSize 
RequiredMosaic 
VVVDR5 
The final pixel size of the mosaic 
real 
4 
arcsec 

?? 
pixelSize 
RequiredMosaic 
VVVv20100531 
The final pixel size of the mosaic 
real 
4 
arcsec 

?? 
pixelSize 
RequiredMosaic 
VVVv20110718 
The final pixel size of the mosaic 
real 
4 
arcsec 

?? 
pixelSize 
RequiredMosaicTopLevel 
ULTRAVISTADR4 
The final pixel size of the mosaic 
real 
4 
arcsec 
0.9999995e9 
?? 
pixelSize 
RequiredMosaicTopLevel 
VVVDR5 
The final pixel size of the mosaic 
real 
4 
arcsec 
0.9999995e9 
?? 
pixSizeAng 
ThreeDimExtinctionMaps 
EXTINCT 
Angular resolution of extinction map 
real 
4 
Arcminutes 
0.9999995e9 

pixSizeRad 
ThreeDimExtinctionMaps 
EXTINCT 
Radial resolution of extinction map 
real 
4 
kpc 
0.9999995e9 

pJKs 
vvvPsfDaophotJKsSource 
VVVDR4 
The fraction of the number of "recovered" vs injected stars per (JKs)  Ks bin {catalogue TType keyword: p} 
real 
4 

0.9999995e9 

pJKs 
vvvPsfDaophotJKsSource 
VVVDR5 
The fraction of the number of "recovered" vs injected stars per (JKs)  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 
plx 
hipparcos_new_reduction 
GAIADR1 
Parallax 
float 
8 
milliarcseconds 

pos.parallax 
plx 
vvvParallaxCatalogue 
VVVDR4 
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 

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 
vvvParallaxCatalogue 
VVVDR5 
Total Proper motion {catalogue TType keyword: pm} 
float 
8 
mas/yr 
999999500.0 

pm 
vvvParallaxCatalogue, vvvProperMotionCatalogue 
VVVDR4 
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_sc2 
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 blendswap took place, and the distance in hundredths of an arcsecond between the nonmotion position and the motion meanobservationepoch 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 blendswap occurred (i.e., the original primary component was not the final primary component), and DDD is the radial distance between the nonmotion and motion at meanobservation epoch positions in units of 0.01 arcsec, clipped at 999 (almost 10 arcsec). For example, a wellbehaved 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 stationaryfit and motion position differ by a significant amount would have a pmcode value like "3Y234". 
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 
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_sc2 
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, 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, tgas_source 
GAIADR1 
Error of proper motion in Declination direction 
float 
8 
milliarcsec/year 

stat.error;pos.pm;.pos.eq.dec 
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_sc2 
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, 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, 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, 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_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 sourcefree individualband 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 individualband 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 sourcefree individualband 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 individualband 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 sourcefree individualband 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 individualband 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 sourcefree individualband 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 individualband 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 sourcefree individualband 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 individualband 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 submodes), 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 submodes), and large window mode with only parts of the FOV exposed. 
varchar 
50 



pNearH 
iras_psc 
IRAS 
Number of nearby hoursconfirmed point sources 
tinyint 
1 


meta.number 
pNearW 
iras_psc 
IRAS 
Number of nearby weeksconfirmed point sources 
tinyint 
1 


meta.number 
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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 
VIKINGDR5 
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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 
VVVDR4 
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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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_POSEQ 
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 
ULTRAVISTADR4 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSDR1 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSDR2 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSDR3 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSDR4 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSDR5 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSDR6 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20120926 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20130417 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20140409 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20150108 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20160114 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20160507 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20170630 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VHSv20180419 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIDEODR2 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIDEODR3 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIDEODR4 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIDEODR5 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIDEOv20100513 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIDEOv20111208 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGDR2 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGDR3 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGDR4 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGDR5 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20110714 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20111019 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20130417 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20140402 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20150421 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20151230 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20160406 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20161202 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VIKINGv20170715 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCDR1 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCDR2 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCDR3 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCDR4 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20110816 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20110909 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20120126 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20121128 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20130304 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20130805 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20140428 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20140903 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20150309 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20151218 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20160311 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20160822 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20170109 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20170411 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20171101 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20180702 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VMCv20181120 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VVVDR1 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VVVDR2 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VVVDR4 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VVVDR5 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VVVv20100531 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
CurrentAstrometry 
VVVv20110718 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
posAngle 
RequiredMosaic 
VHSDR1 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSDR2 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSDR3 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSDR4 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSDR5 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSDR6 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSv20120926 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSv20130417 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSv20150108 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSv20160114 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSv20160507 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSv20170630 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VHSv20180419 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIDEODR2 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIDEODR3 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIDEODR4 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIDEODR5 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIDEOv20111208 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGDR2 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGDR3 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGDR4 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGDR5 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20110714 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20111019 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20130417 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20150421 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20151230 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20160406 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20161202 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VIKINGv20170715 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCDR1 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCDR3 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCDR4 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20110816 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20110909 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20120126 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20121128 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20130304 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20130805 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20140428 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20140903 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20150309 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20151218 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20160311 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20160822 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20170109 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20170411 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20171101 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20180702 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VMCv20181120 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VVVDR1 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VVVDR2 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VVVDR4 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VVVDR5 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic 
VVVv20110718 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
RequiredMosaic, RequiredRegion, RequiredStack, RequiredTile 
ULTRAVISTADR4 
Orientation of image xaxis to NS 
real 
4 
deg 
0.9999995e9 

posAngle 
ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry 
VSAQC 
orientation of image xaxis to NS 
float 
8 
Degrees 
0.9999995e9 
pos.posAng 
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 
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 
VIKINGDR5 
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 
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 
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 
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 
VVVDR4 
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 
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 
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 
ultravistaDetection, ultravistaMapRemeasurement 
ULTRAVISTADR4 
additional WFAU postprocessing error bits 
int 
4 

0 
meta.code 
Postprocessing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4byte 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 postprocessing error bits based on combining average pawprint and tile flagging 
int 
4 

0 
meta.code 
Postprocessing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4byte 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 postprocessing error bits 
int 
4 

0 
meta.code 
Postprocessing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4byte 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 postprocessing error bits 
int 
4 

0 
meta.code 
Postprocessing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4byte 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  