P |
Name | Schema Table | Database | Description | Type | Length | Unit | Default Value | Unified Content Descriptor |
p1 |
catwise_2020, catwise_prelim |
WISE |
P vector component 1 |
real |
4 |
arcsec |
|
|
p1 |
cepheid, rrlyrae |
GAIADR1 |
Period corresponding to the maximum peak in the periodogram of G band time series |
float |
8 |
days |
|
time.period |
p1_error |
cepheid, rrlyrae |
GAIADR1 |
Uncertainty on the period corresponding to the maximum peak in the periodogram of G band time series |
float |
8 |
days |
|
stat.error;time.period |
p2 |
catwise_2020, catwise_prelim |
WISE |
P vector component 2 |
real |
4 |
arcsec |
|
|
PA |
combo17CDFSSource |
COMBO17 |
position angle, measured West to North |
real |
4 |
deg |
|
|
PA |
nvssSource |
NVSS |
[-90, 90] Position angle of fitted major axis |
real |
4 |
degress |
|
pos.posAng |
pa |
first08Jul16Source, firstSource, firstSource12Feb16 |
FIRST |
position angle (east of north) derived from the elliptical Gaussian model for the source |
real |
4 |
degrees |
|
pos.posAng |
pa |
sharksDetection |
SHARKSv20210222 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
sharksDetection |
SHARKSv20210421 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
ultravistaDetection, ultravistaMapRemeasurement |
ULTRAVISTADR4 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
ultravistaMapRemeasAver |
ULTRAVISTADR4 |
Averaged ellipse fit orientation to x axis Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSDR2 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSDR3 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSDR4 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSDR5 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSDR6 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20120926 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20130417 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20140409 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20150108 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20160114 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20160507 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20170630 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20180419 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20201209 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection |
VHSv20231101 |
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 |
VHSv20240731 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
videoDetection |
VIDEODR2 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
videoDetection |
VIDEODR3 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
videoDetection |
VIDEODR4 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
videoDetection |
VIDEODR5 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
videoDetection |
VIDEOv20100513 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
videoDetection |
VIDEOv20111208 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
videoListRemeasurement |
VIDEOv20100513 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGDR2 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGDR3 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGDR4 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20111019 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20130417 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20140402 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20150421 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20151230 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20160406 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20161202 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection |
VIKINGv20170715 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingMapRemeasAver |
VIKINGZYSELJv20160909 |
Averaged ellipse fit orientation to x axis Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingMapRemeasAver |
VIKINGZYSELJv20170124 |
Averaged ellipse fit orientation to x axis Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis counterclockwise. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCDR1 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCDR2 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCDR3 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCDR4 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCDR5 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20110909 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20120126 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20121128 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20130304 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20130805 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20140428 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20140903 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20150309 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20151218 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20160311 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20160822 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20170109 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20170411 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20171101 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20180702 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20181120 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20191212 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20210708 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20230816 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection |
VMCv20240226 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcdeepDetection |
VMCDEEPv20230713 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vmcdeepDetection |
VMCDEEPv20240506 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vvvDetection |
VVVDR1 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vvvDetection |
VVVDR2 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
ellipse fit orientation to x axis {catalogue TType keyword: Position_angle} Angle of ellipse major axis wrt x axis. |
real |
4 |
degrees |
|
pos.posAng |
pa_2mass |
allwise_sc |
WISE |
Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source. |
float |
8 |
deg |
|
|
pa_2mass |
wise_allskysc |
WISE |
Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source, default if there is no associated 2MASS PSC source. |
real |
4 |
degrees |
-0.9999995e9 |
|
pa_2mass |
wise_prelimsc |
WISE |
Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source, default if there is no associated 2MASS PSC source |
real |
4 |
degrees |
-0.9999995e9 |
|
pAGB |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being an AGB star. {catalogue TType keyword: AGB} |
float |
8 |
|
|
|
pAGBErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being an AGB star. {catalogue TType keyword: AGB_err} |
float |
8 |
|
|
|
pAGBorRGB |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being a post-AGB or post-RGB star. {catalogue TType keyword: pAGB/pRGB} |
float |
8 |
|
|
|
pAGBorRGBErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being a post-AGB or post-RGB star. {catalogue TType keyword: pAGB/pRGB_err} |
float |
8 |
|
|
|
pAGN |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being an AGN. {catalogue TType keyword: AGN} |
float |
8 |
|
|
|
pAGNErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being an AGN. {catalogue TType keyword: AGN_err} |
float |
8 |
|
|
|
pairingCriterion |
Programme |
SHARKSv20210222 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
SHARKSv20210421 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
ULTRAVISTADR4 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSDR1 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSDR2 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSDR3 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSDR4 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSDR5 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSDR6 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20120926 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20130417 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20150108 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20160114 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20160507 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20170630 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20180419 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20201209 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20231101 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VHSv20240731 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIDEODR2 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIDEODR3 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIDEODR4 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIDEODR5 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIDEOv20100513 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIDEOv20111208 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGDR2 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGDR3 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGDR4 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20110714 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20111019 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20130417 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20150421 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20151230 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20160406 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20161202 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VIKINGv20170715 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCDEEPv20230713 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCDEEPv20240506 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCDR1 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCDR3 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCDR4 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCDR5 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20110816 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20110909 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20120126 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20121128 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20130304 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20130805 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20140428 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20140903 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20150309 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20151218 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20160311 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20160822 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20170109 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20170411 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20171101 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20180702 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20181120 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20191212 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20210708 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20230816 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VMCv20240226 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VSAQC |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VVVDR1 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VVVDR2 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VVVDR5 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VVVXDR1 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VVVv20100531 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
pairingCriterion |
Programme |
VVVv20110718 |
The pairing criterion for associating detections into merged sources |
real |
4 |
Degrees |
|
?? |
par_pm |
catwise_2020, catwise_prelim |
WISE |
parallax from PM desc-asce elon |
real |
4 |
arcsec |
|
|
par_pm is computed from the motion-solution positions, which are translated by WPHotpmc to the standard epoch (MJD0), so except for estimation errors, par_pm is the parallax; par_pm will be null unless km = 3. |
par_pmSig |
catwise_2020, catwise_prelim |
WISE |
one-sigma uncertainty in par_pm |
real |
4 |
arcsec |
|
|
par_sigma |
catwise_2020, catwise_prelim |
WISE |
one-sigma uncertainty in par_stat |
real |
4 |
arcsec |
|
|
par_stat |
catwise_2020, catwise_prelim |
WISE |
parallax estimate from stationary solution |
real |
4 |
arcsec |
|
|
The par_stat column is computed by using the motion estimate to move the ascending stationary-solution position from the ascending effective observation epoch to that of the descending solution, then dividing the ecliptic longitude difference by 2; par_stat will be null unless ka = 3 AND km > 0 AND all W?mJDmin/max/mean values are non-null in both ascending and descending mdex files. |
parallax |
gaia_source |
GAIADR2 |
Parallax |
float |
8 |
milliarcsec |
|
pos.parallax |
parallax |
gaia_source |
GAIAEDR3 |
Parallax |
float |
8 |
milliarcsec |
|
pos.parallax |
parallax |
gaia_source, tgas_source |
GAIADR1 |
Parallax |
float |
8 |
milliarcsec |
|
pos.parallax |
parallax |
ravedr5Source |
RAVE |
spectrophotometric Parallax (Binney et al. 2014) |
real |
4 |
mas |
|
pos.parallax |
parallax |
sharksVariability |
SHARKSv20210222 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
sharksVariability |
SHARKSv20210421 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
ultravistaVariability |
ULTRAVISTADR4 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
videoVariability |
VIDEODR2 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
videoVariability |
VIDEODR3 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
videoVariability |
VIDEODR4 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
videoVariability |
VIDEODR5 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
videoVariability |
VIDEOv20100513 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
videoVariability |
VIDEOv20111208 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGDR2 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGDR3 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGDR4 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20110714 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20111019 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20130417 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20140402 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20150421 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20151230 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20160406 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20161202 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vikingVariability |
VIKINGv20170715 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCDR1 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCDR2 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCDR3 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCDR4 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCDR5 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20110816 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20110909 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20120126 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20121128 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20130304 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20130805 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20140428 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20140903 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20150309 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20151218 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20160311 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20160822 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20170109 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20170411 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20171101 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20180702 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20181120 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20191212 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20210708 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20230816 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcVariability |
VMCv20240226 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcdeepVariability |
VMCDEEPv20230713 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vmcdeepVariability |
VMCDEEPv20240506 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vvvVariability |
VVVDR1 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vvvVariability |
VVVDR2 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vvvVariability |
VVVDR5 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vvvVariability |
VVVv20100531 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vvvVariability |
VVVv20110718 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
|
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax |
vvvxVariability |
VVVXDR1 |
Parallax of star |
real |
4 |
mas |
-0.9999995e9 |
pos.parallax |
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. |
parallax_error |
gaia_source |
GAIADR2 |
Standard error of parallax |
float |
8 |
milliarcsec |
|
stat.error;pos.parallax |
parallax_error |
gaia_source |
GAIAEDR3 |
Standard error of parallax |
float |
8 |
milliarcsec |
|
stat.error;pos.parallax |
parallax_error |
gaia_source, tgas_source |
GAIADR1 |
Standard error of parallax |
float |
8 |
milliarcsec |
|
stat.error;pos.parallax |
parallax_error_TGAS |
ravedr5Source |
RAVE |
Error of parallax |
float |
8 |
mas |
|
stat.error;pos.parallax |
parallax_over_error |
gaia_source |
GAIADR2 |
Parallax divided by standard error |
real |
4 |
|
|
arith.ratio |
parallax_over_error |
gaia_source |
GAIAEDR3 |
Parallax divided by standard error |
real |
4 |
|
|
arith.ratio |
parallax_pmdec_corr |
gaia_source |
GAIADR2 |
Correlation between parallax and proper motion in Declination |
real |
4 |
|
|
stat.correlation;pos.parallax;pos.pm;pos.eq.dec |
parallax_pmdec_corr |
gaia_source |
GAIAEDR3 |
Correlation between parallax and proper motion in Declination |
real |
4 |
|
|
stat.correlation;pos.parallax;pos.pm;pos.eq.dec |
parallax_pmdec_corr |
gaia_source, tgas_source |
GAIADR1 |
Correlation between parallax and proper motion in Declination |
real |
4 |
|
|
stat.correlation |
parallax_pmra_corr |
gaia_source |
GAIADR2 |
Correlation between parallax and proper motion in Right Ascension |
real |
4 |
|
|
stat.correlation;pos.parallax;pos.pm;pos.eq.ra |
parallax_pmra_corr |
gaia_source |
GAIAEDR3 |
Correlation between parallax and proper motion in Right Ascension |
real |
4 |
|
|
stat.correlation;pos.parallax;pos.pm;pos.eq.ra |
parallax_pmra_corr |
gaia_source, tgas_source |
GAIADR1 |
Correlation between parallax and proper motion in Right Ascension |
real |
4 |
|
|
stat.correlation |
parallax_pseudocolour_corr |
gaia_source |
GAIAEDR3 |
Correlation between parallax and pseudocolour |
real |
4 |
|
|
stat.correlation;em.wavenumber;pos.parallax |
parallax_TGAS |
ravedr5Source |
RAVE |
Parallax |
float |
8 |
mas |
|
pos.parallax |
paramTemplate |
RequiredMosaicTopLevel |
SHARKSv20210222 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
SHARKSv20210421 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
ULTRAVISTADR4 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VHSv20201209 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VHSv20231101 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VHSv20240731 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VMCDEEPv20230713 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VMCDEEPv20240506 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VMCDR5 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VMCv20191212 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VMCv20210708 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VMCv20230816 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VMCv20240226 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VVVDR5 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
paramTemplate |
RequiredMosaicTopLevel |
VVVXDR1 |
Template file for SWARP parameters |
varchar |
32 |
|
|
|
PARK |
grs_ngpSource, grs_ranSource, grs_sgpSource |
TWODFGRS |
k classification parameter = k / k_star |
real |
4 |
|
|
|
PARMU |
grs_ngpSource, grs_ranSource, grs_sgpSource |
TWODFGRS |
mu classification parameter = mu / mu_star |
real |
4 |
|
|
|
patternString |
Multiframe |
SHARKSv20210222 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
SHARKSv20210421 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
ULTRAVISTADR4 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSDR1 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSDR2 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSDR3 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSDR4 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSDR5 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSDR6 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20120926 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20130417 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20140409 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20150108 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20160114 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20160507 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20170630 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20180419 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20201209 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20231101 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VHSv20240731 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIDEODR2 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIDEODR3 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIDEODR4 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIDEODR5 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIDEOv20111208 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGDR2 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGDR3 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGDR4 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20110714 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20111019 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20130417 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20140402 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20150421 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20151230 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20160406 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20161202 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VIKINGv20170715 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCDEEPv20230713 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCDEEPv20240506 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCDR1 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCDR2 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCDR3 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCDR4 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCDR5 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20110816 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20110909 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20120126 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20121128 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20130304 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20130805 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20140428 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20140903 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20150309 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20151218 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20160311 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20160822 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20170109 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20170411 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20171101 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20180702 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20181120 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20191212 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20210708 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20230816 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VMCv20240226 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VVVDR1 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VVVDR2 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VVVDR5 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VVVXDR1 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
Multiframe |
VVVv20110718 |
SADT pattern ID {image primary HDU keyword: HIERARCH ESO OCS SADT PATTERN} |
varchar |
64 |
|
NONE |
|
patternString |
sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe |
VSAQC |
SADT pattern ID |
varchar |
64 |
|
NONE |
|
pawprintdets |
vvvParallaxCatalogue, vvvProperMotionCatalogue |
VVVDR5 |
the number of separate pawprint sets in which a source was detected. Technically 'dets' can be greater than this value where e.g. a high proper motion or faint source is not matched between consecutive observing seasons. {catalogue TType keyword: pawprintdets} |
int |
4 |
|
-99999999 |
|
peak_to_peak_g |
cepheid, rrlyrae |
GAIADR1 |
Peak-to-peak amplitude of the G band light curve |
float |
8 |
mag |
|
src.var.amplitude;em.opt |
peak_to_peak_g_error |
cepheid, rrlyrae |
GAIADR1 |
Uncertainty on peak-to-peak amplitude of the G band light curve |
float |
8 |
mag |
|
stat.error;src.var.amplitude;em.opt |
perErr |
ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource |
OGLE |
Uncertainty of period |
float |
8 |
days |
|
stat.error;time.duration |
period |
ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource |
OGLE |
Period |
float |
8 |
days |
|
time.period |
period |
vmcCepheidVariables |
VMCDR3 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCDR4 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20121128 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20140428 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20140903 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20150309 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20151218 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20160311 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20160822 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20170109 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20170411 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20171101 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20180702 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20181120 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20191212 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20210708 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20230816 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcCepheidVariables |
VMCv20240226 |
Period of first mode of oscillation {catalogue TType keyword: Period} |
real |
4 |
day |
-0.9999995e9 |
time.period |
period |
vmcEclipsingBinaryVariables |
VMCDR4 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20140903 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20150309 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20151218 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20160311 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20160822 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20170109 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20170411 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20171101 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20180702 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20181120 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20191212 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20210708 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20230816 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcEclipsingBinaryVariables |
VMCv20240226 |
Period from the EROS/OGLE catalogues. Periods of some stars (marked *, in externalID) were recalculated using GRATIS {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRLyraeVariables |
VMCv20240226 |
Period {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCDR4 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20160822 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20170109 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20170411 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20171101 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20180702 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20181120 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20191212 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20210708 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period |
vmcRRlyraeVariables |
VMCv20230816 |
Period from OGLE-3 survey {catalogue TType keyword: PERIOD} |
real |
4 |
day |
|
time.period |
period1 |
ogle3LpvLmcSource, ogle3LpvSmcSource |
OGLE |
Primary period |
float |
8 |
days |
|
time.period |
period2 |
ogle3LpvLmcSource, ogle3LpvSmcSource |
OGLE |
Secondary period (detected automatically) |
float |
8 |
days |
|
time.period |
period3 |
ogle3LpvLmcSource, ogle3LpvSmcSource |
OGLE |
Tertiary period (detected automatically) |
float |
8 |
days |
|
time.period |
petroFlux |
sharksDetection |
SHARKSv20210222 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
sharksDetection |
SHARKSv20210421 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
ultravistaDetection |
ULTRAVISTADR4 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO; CASU: default) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSDR2 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vhsDetection |
VHSDR3 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSDR4 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSDR5 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSDR6 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20120926 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20130417 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20140409 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20150108 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20160114 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20160507 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20170630 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20180419 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20201209 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20231101 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection |
VHSv20240731 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
videoDetection |
VIDEODR2 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
videoDetection |
VIDEODR3 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
videoDetection |
VIDEODR4 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
videoDetection |
VIDEODR5 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
videoDetection |
VIDEOv20100513 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
videoDetection |
VIDEOv20111208 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
videoListRemeasurement |
VIDEOv20100513 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vikingDetection |
VIKINGDR2 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vikingDetection |
VIKINGDR3 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGDR4 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGv20111019 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vikingDetection |
VIKINGv20130417 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGv20140402 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGv20150421 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGv20151230 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGv20160406 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGv20161202 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection |
VIKINGv20170715 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO; CASU: default) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
flux within Petrosian radius circular aperture (SE: FLUX_PETRO; CASU: default) {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vmcDetection |
VMCDR1 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vmcDetection |
VMCDR2 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCDR3 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCDR4 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCDR5 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20110909 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vmcDetection |
VMCv20120126 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vmcDetection |
VMCv20121128 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20130304 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20130805 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20140428 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20140903 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20150309 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20151218 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20160311 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20160822 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20170109 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20170411 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20171101 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20180702 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20181120 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20191212 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20210708 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20230816 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection |
VMCv20240226 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFlux |
vmcdeepDetection |
VMCDEEPv20230713 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vmcdeepDetection |
VMCDEEPv20240506 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vvvDetection |
VVVDR1 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vvvDetection |
VVVDR2 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count |
petroFlux |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} |
real |
4 |
ADU |
|
phot.count;em.opt |
petroFluxErr |
sharksDetection |
SHARKSv20210222 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
sharksDetection |
SHARKSv20210421 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
ultravistaDetection |
ULTRAVISTADR4 |
error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
error on Petrosian flux (SE: FLUXERR_PETRO; CASU: default) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSDR2 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSDR3 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSDR4 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSDR5 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSDR6 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20120926 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20130417 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20140409 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20150108 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20160114 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20160507 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20170630 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20180419 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20201209 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20231101 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection |
VHSv20240731 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
videoDetection |
VIDEODR2 |
error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
videoDetection |
VIDEODR3 |
error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
videoDetection |
VIDEODR4 |
error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
videoDetection |
VIDEODR5 |
error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
videoDetection |
VIDEOv20100513 |
error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
videoDetection |
VIDEOv20111208 |
error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
videoListRemeasurement |
VIDEOv20100513 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGDR2 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGDR3 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGDR4 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20111019 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20130417 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20140402 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20150421 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20151230 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20160406 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20161202 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection |
VIKINGv20170715 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
error on Petrosian flux (SE: FLUXERR_PETRO; CASU: default) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
error on Petrosian flux (SE: FLUXERR_PETRO; CASU: default) {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCDR1 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCDR2 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCDR3 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCDR4 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCDR5 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20110909 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20120126 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20121128 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20130304 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20130805 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20140428 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20140903 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20150309 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20151218 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20160311 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20160822 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20170109 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20170411 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20171101 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20180702 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20181120 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20191212 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20210708 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20230816 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection |
VMCv20240226 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcdeepDetection |
VMCDEEPv20230713 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vmcdeepDetection |
VMCDEEPv20240506 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vvvDetection |
VVVDR1 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vvvDetection |
VVVDR2 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroFluxErr |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
error on Petrosian flux {catalogue TType keyword: Petr_flux_err} |
real |
4 |
ADU |
|
stat.error |
petroJky |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
Calibrated Petrosian flux within aperture r_p (CASU: default) |
real |
4 |
jansky |
|
phot.mag |
petroJky |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
Calibrated Petrosian flux within aperture r_p (CASU: default) |
real |
4 |
jansky |
|
phot.mag |
petroJky |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
Calibrated Petrosian flux within aperture r_p (CASU: default) |
real |
4 |
jansky |
|
phot.mag |
petroJkyErr |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
error on calibrated Petrosian flux (CASU: default) |
real |
4 |
jansky |
|
stat.error |
petroJkyErr |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
error on calibrated Petrosian flux (CASU: default) |
real |
4 |
jansky |
|
stat.error |
petroJkyErr |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
error on calibrated Petrosian flux (CASU: default) |
real |
4 |
jansky |
|
stat.error |
petroLup |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
Calibrated Petrosian luptitude within aperture r_p (CASU: default) |
real |
4 |
lup |
|
phot.mag |
petroLup |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
Calibrated Petrosian luptitude within aperture r_p (CASU: default) |
real |
4 |
lup |
|
phot.mag |
petroLup |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
Calibrated Petrosian luptitude within aperture r_p (CASU: default) |
real |
4 |
lup |
|
phot.mag |
petroLupErr |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
error on calibrated Petrosian luptitude (CASU: default) |
real |
4 |
lup |
|
stat.error |
petroLupErr |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
error on calibrated Petrosian luptitude (CASU: default) |
real |
4 |
lup |
|
stat.error |
petroLupErr |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
error on calibrated Petrosian luptitude (CASU: default) |
real |
4 |
lup |
|
stat.error |
petroMag |
sharksDetection |
SHARKSv20210222 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
sharksDetection |
SHARKSv20210421 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
ultravistaDetection |
ULTRAVISTADR4 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
Calibrated Petrosian magnitude within aperture r_p (CASU: default) |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSDR2 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSDR3 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSDR4 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSDR5 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSDR6 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20120926 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20130417 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20140409 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20150108 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20160114 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20160507 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20170630 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20180419 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20201209 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20231101 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection |
VHSv20240731 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
videoDetection |
VIDEODR2 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
videoDetection |
VIDEODR3 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
videoDetection |
VIDEODR4 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
videoDetection |
VIDEODR5 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
videoDetection |
VIDEOv20111208 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
videoDetection, videoListRemeasurement |
VIDEOv20100513 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGDR2 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGDR3 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGDR4 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20111019 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20130417 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20140402 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20150421 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20151230 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20160406 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20161202 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection |
VIKINGv20170715 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
Calibrated Petrosian magnitude within aperture r_p (CASU: default) |
real |
4 |
mag |
|
phot.mag |
petroMag |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
Calibrated Petrosian magnitude within aperture r_p (CASU: default) |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCDR1 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCDR2 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCDR3 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCDR4 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCDR5 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20110909 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20120126 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20121128 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20130304 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20130805 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20140428 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20140903 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20150309 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20151218 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20160311 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20160822 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20170109 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20170411 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20171101 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20180702 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20181120 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20191212 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20210708 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20230816 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection |
VMCv20240226 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcdeepDetection |
VMCDEEPv20230713 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vmcdeepDetection |
VMCDEEPv20240506 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vvvDetection |
VVVDR1 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vvvDetection |
VVVDR2 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMag |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
Calibrated Petrosian magnitude within circular aperture r_p |
real |
4 |
mag |
|
phot.mag |
petroMagErr |
sharksDetection |
SHARKSv20210222 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
sharksDetection |
SHARKSv20210421 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
ultravistaDetection |
ULTRAVISTADR4 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
error on calibrated Petrosian magnitude (CASU: default) |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vhsDetection |
VHSDR2 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vhsDetection |
VHSDR3 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vhsDetection |
VHSDR4 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSDR5 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSDR6 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20120926 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vhsDetection |
VHSv20130417 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vhsDetection |
VHSv20140409 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vhsDetection |
VHSv20150108 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20160114 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20160507 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20170630 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20180419 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20201209 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20231101 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection |
VHSv20240731 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
videoDetection |
VIDEODR2 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
videoDetection |
VIDEODR3 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
videoDetection |
VIDEODR4 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
videoDetection |
VIDEODR5 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
videoDetection |
VIDEOv20111208 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
videoDetection, videoListRemeasurement |
VIDEOv20100513 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingDetection |
VIKINGDR2 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingDetection |
VIKINGDR3 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingDetection |
VIKINGDR4 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingDetection |
VIKINGv20111019 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingDetection |
VIKINGv20130417 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingDetection |
VIKINGv20140402 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingDetection |
VIKINGv20150421 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vikingDetection |
VIKINGv20151230 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vikingDetection |
VIKINGv20160406 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vikingDetection |
VIKINGv20161202 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vikingDetection |
VIKINGv20170715 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
error on calibrated Petrosian magnitude (CASU: default) |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
error on calibrated Petrosian magnitude (CASU: default) |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCDR1 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCDR2 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCDR3 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCDR4 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCDR5 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20110909 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCv20120126 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCv20121128 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCv20130304 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCv20130805 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCv20140428 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcDetection |
VMCv20140903 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20150309 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20151218 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20160311 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20160822 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20170109 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20170411 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20171101 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20180702 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20181120 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20191212 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20210708 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20230816 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection |
VMCv20240226 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vmcdeepDetection |
VMCDEEPv20230713 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vmcdeepDetection |
VMCDEEPv20240506 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vvvDetection |
VVVDR1 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vvvDetection |
VVVDR2 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroMagErr |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error;phot.mag |
petroMagErr |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
error on calibrated Petrosian magnitude |
real |
4 |
mag |
|
stat.error |
petroRad |
sharksDetection |
SHARKSv20210222 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
sharksDetection |
SHARKSv20210421 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
ultravistaDetection |
ULTRAVISTADR4 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
ultravistaMapRemeasurement |
ULTRAVISTADR4 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
vhsDetection |
VHSDR2 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vhsDetection |
VHSDR3 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSDR4 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSDR5 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSDR6 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20120926 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20130417 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20140409 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20150108 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20160114 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20160507 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20170630 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20180419 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20201209 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20231101 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection |
VHSv20240731 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
videoDetection |
VIDEODR2 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
videoDetection |
VIDEODR3 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
videoDetection |
VIDEODR4 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
videoDetection |
VIDEODR5 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
videoDetection |
VIDEOv20100513 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
videoDetection |
VIDEOv20111208 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
videoListRemeasurement |
VIDEOv20100513 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vikingDetection |
VIKINGDR2 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vikingDetection |
VIKINGDR3 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGDR4 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGv20111019 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vikingDetection |
VIKINGv20130417 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGv20140402 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGv20150421 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGv20151230 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGv20160406 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGv20161202 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection |
VIKINGv20170715 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
Petrosian radius (SE: PETRO_RADIUS*A_IMAGE; CASU: default) {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels. |
petroRad |
vmcDetection |
VMCDR1 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vmcDetection |
VMCDR2 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCDR3 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCDR4 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCDR5 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20110909 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vmcDetection |
VMCv20120126 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vmcDetection |
VMCv20121128 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20130304 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20130805 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20140428 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20140903 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20150309 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20151218 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20160311 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20160822 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20170109 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20170411 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20171101 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20180702 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20181120 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20191212 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20210708 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20230816 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection |
VMCv20240226 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
petroRad |
vmcdeepDetection |
VMCDEEPv20230713 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vmcdeepDetection |
VMCDEEPv20240506 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vvvDetection |
VVVDR1 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vvvDetection |
VVVDR2 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize |
petroRad |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} |
real |
4 |
pixels |
|
phys.angSize;src |
PF_DEC |
mgcBrightSpec |
MGC |
PFr object declination in deg (J2000) |
float |
8 |
|
|
|
PF_JMK |
mgcBrightSpec |
MGC |
PFr J-K colour from 2MASS |
real |
4 |
|
|
|
PF_K |
mgcBrightSpec |
MGC |
PFr K magnitude from 2MASS |
real |
4 |
|
|
|
PF_NAME |
mgcBrightSpec |
MGC |
PFr object name |
varchar |
8 |
|
|
|
PF_R |
mgcBrightSpec |
MGC |
PFr R magnitude from USNO |
real |
4 |
|
|
|
PF_RA |
mgcBrightSpec |
MGC |
PFr object right ascension in deg (J2000) |
float |
8 |
|
|
|
PF_Z |
mgcBrightSpec |
MGC |
PFr redshift |
real |
4 |
|
|
|
PF_ZQUAL |
mgcBrightSpec |
MGC |
PFr redshift quality |
tinyint |
1 |
|
|
|
pFlag |
rosat_bsc, rosat_fsc |
ROSAT |
possible problem with position determination |
varchar |
1 |
|
|
meta.code |
pflag |
tycho2 |
GAIADR1 |
Mean position flag |
varchar |
1 |
|
|
meta.code |
pGalaxy |
sharksSource |
SHARKSv20210222 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
sharksSource |
SHARKSv20210421 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
ultravistaSource, ultravistaSourceRemeasurement |
ULTRAVISTADR4 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSDR1 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSDR2 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSDR3 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSDR4 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSDR5 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSDR6 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20120926 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20130417 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20140409 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20150108 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20160114 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20160507 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20170630 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20180419 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vhsSource |
VHSv20201209 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 |
VHSv20231101 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 |
VHSv20240731 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
videoSource |
VIDEODR2 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
videoSource |
VIDEODR3 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
videoSource |
VIDEODR4 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
videoSource |
VIDEODR5 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
videoSource |
VIDEOv20100513 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
videoSource |
VIDEOv20111208 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGDR2 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGDR3 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGDR4 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20110714 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20111019 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20130417 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20140402 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20150421 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20151230 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20160406 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20161202 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingSource |
VIKINGv20170715 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20160909 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20170124 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being an Galaxy. {catalogue TType keyword: Galaxy} |
float |
8 |
|
|
|
pGalaxy |
vmcSource |
VMCDR2 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCDR3 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCDR4 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCDR5 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20110816 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20110909 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20120126 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20121128 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20130304 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20130805 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20140428 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20140903 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20150309 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20151218 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20160311 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20160822 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20170109 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20170411 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20171101 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20180702 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20181120 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20191212 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20210708 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20230816 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource |
VMCv20240226 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcSource, vmcSynopticSource |
VMCDR1 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcdeepSource |
VMCDEEPv20240506 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vmcdeepSource, vmcdeepSynopticSource |
VMCDEEPv20230713 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vvvSource |
VVVDR2 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vvvSource |
VVVDR5 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vvvSource |
VVVv20100531 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vvvSource |
VVVv20110718 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxy |
vvvSource, vvvSynopticSource |
VVVDR1 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 |
vvvxSource |
VVVXDR1 |
Probability that the source is a galaxy |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pGalaxyErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being an Galaxy. {catalogue TType keyword: Galaxy_err} |
float |
8 |
|
|
|
ph_qual |
allwise_sc |
WISE |
Photometric quality flag. Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio. |
varchar |
4 |
|
|
|
- A - Source is detected in this band with a flux signal-to-noise ratio w?snr>10.
- B - Source is detected in this band with a flux signal-to-noise ratio 3<w?snr<10.
- C - Source is detected in this band with a flux signal-to-noise ratio 2<w?snr<3.
- U - Upper limit on magnitude. Source measurement has w?snr<2. The profile-fit magnitude w?mpro is a 95% confidence upper limit.
- X - A profile-fit measurement was not possible at this location in this band. The value of w?mpro and w?sigmpro will be "null" in this band.
- Z - A profile-fit source flux measurement was made at this location, but the flux uncertainty could not be measured. The value of w?sigmpro will be "null" in this band. The value of w?mpro will be "null" if the measured flux, w?flux, is negative, but will not be "null" if the flux is positive. If a non-null magnitude is present, it corresponds to the true flux, and not the 95% confidence upper limit. This occurs for a small number of sources found in a narrow range of ecliptic longitude which were covered by a large number of saturated pixels from 3-Band Cryo single-exposures.
|
ph_qual |
twomass_psc |
TWOMASS |
Photometric quality flag. |
varchar |
3 |
|
|
meta.code.qual |
ph_qual |
twomass_sixx2_psc |
TWOMASS |
flag indicating photometric quality of source |
varchar |
3 |
|
|
|
ph_qual |
wise_allskysc |
WISE |
Photometric quality flag. Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio. |
char |
4 |
|
|
|
ph_qual |
wise_prelimsc |
WISE |
Photometric quality flag Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio |
char |
4 |
|
|
|
ph_qual_ALLWISE |
ravedr5Source |
RAVE |
photometric quality of each band (A=highest, U=upper limit) |
varchar |
5 |
|
|
meta.code_mag |
phaRange |
rosat_bsc, rosat_fsc |
ROSAT |
PHA range with highest detection likelihood |
varchar |
1 |
|
|
meta.code |
pHeight |
sharksDetection |
SHARKSv20210222 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
sharksDetection |
SHARKSv20210421 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
ultravistaDetection, ultravistaMapRemeasurement |
ULTRAVISTADR4 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSDR2 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSDR3 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSDR4 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSDR5 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSDR6 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20120926 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20130417 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20140409 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20150108 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20160114 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20160507 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20170630 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20180419 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20201209 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection |
VHSv20231101 |
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 |
VHSv20240731 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
videoDetection |
VIDEODR2 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
videoDetection |
VIDEODR3 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
videoDetection |
VIDEODR4 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
videoDetection |
VIDEODR5 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
videoDetection |
VIDEOv20100513 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
videoDetection |
VIDEOv20111208 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
videoListRemeasurement |
VIDEOv20100513 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGDR2 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGDR3 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGDR4 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20111019 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20130417 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20140402 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20150421 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20151230 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20160406 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20161202 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection |
VIKINGv20170715 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCDR1 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCDR2 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCDR3 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCDR4 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCDR5 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20110909 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20120126 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20121128 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20130304 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20130805 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20140428 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20140903 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20150309 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20151218 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20160311 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20160822 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20170109 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20170411 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20171101 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20180702 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20181120 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20191212 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20210708 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20230816 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection |
VMCv20240226 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcdeepDetection |
VMCDEEPv20230713 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vmcdeepDetection |
VMCDEEPv20240506 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vvvDetection |
VVVDR1 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vvvDetection |
VVVDR2 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeight |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
Highest pixel value above sky {catalogue TType keyword: Peak_height} In counts relative to local value of sky - also zeroth order aperture flux. |
real |
4 |
ADU |
|
phot.count |
pHeightErr |
sharksDetection |
SHARKSv20210222 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
sharksDetection |
SHARKSv20210421 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
ultravistaDetection, ultravistaMapRemeasurement |
ULTRAVISTADR4 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSDR2 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSDR3 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSDR4 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSDR5 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSDR6 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20120926 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20130417 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20140409 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20150108 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20160114 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20160507 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20170630 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20180419 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20201209 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20231101 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection |
VHSv20240731 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
videoDetection |
VIDEODR2 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
videoDetection |
VIDEODR3 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
videoDetection |
VIDEODR4 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
videoDetection |
VIDEODR5 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
videoDetection |
VIDEOv20100513 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
videoDetection |
VIDEOv20111208 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
videoListRemeasurement |
VIDEOv20100513 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGDR2 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGDR3 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGDR4 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20111019 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20130417 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20140402 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20150421 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20151230 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20160406 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20161202 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection |
VIKINGv20170715 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
Error in peak height {catalogue TType keyword: Peak_height_err} FLUX_MAX*FLUXERR_APER1 / FLUX_APER1 |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCDR1 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCDR2 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCDR3 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCDR4 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCDR5 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20110909 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20120126 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20121128 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20130304 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20130805 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20140428 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20140903 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20150309 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20151218 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20160311 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20160822 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20170109 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20170411 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20171101 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20180702 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20181120 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20191212 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20210708 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20230816 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection |
VMCv20240226 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcdeepDetection |
VMCDEEPv20230713 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vmcdeepDetection |
VMCDEEPv20240506 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vvvDetection |
VVVDR1 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vvvDetection |
VVVDR2 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
pHeightErr |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
Error in peak height {catalogue TType keyword: Peak_height_err} |
real |
4 |
ADU |
|
stat.error |
phi21 |
ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource |
OGLE |
Fourier coefficient phi_21 |
real |
4 |
|
|
stat.param |
phi21_g |
cepheid, rrlyrae |
GAIADR1 |
Fourier decomposition parameter phi21G: phi2 - 2*phi1 (for G band) |
float |
8 |
|
|
stat.Fourier |
phi21_g_error |
cepheid, rrlyrae |
GAIADR1 |
Uncertainty on Fourier decomposition parameter phi21G |
float |
8 |
|
|
stat.error |
phi31 |
ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource |
OGLE |
Fourier coefficient phi_31 |
real |
4 |
|
|
stat.param |
phi_opt |
twomass_psc |
TWOMASS |
Position angle on the sky of the vector from the the associated optical source to the TWOMASS source position, in degrees East of North. |
smallint |
2 |
degrees |
|
pos.posAng |
pHighPM |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being a foreground (high proper-motion) star. {catalogue TType keyword: PM} |
float |
8 |
|
|
|
pHighPMErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being a foreground star. {catalogue TType keyword: PM_err} |
float |
8 |
|
|
|
pHIIorYSO |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being a compact HII region or a YSO. {catalogue TType keyword: HII/YSO} |
float |
8 |
|
|
|
pHIIorYSOErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being a compact HII region or a YSO. {catalogue TType keyword: HII/YSO_err} |
float |
8 |
|
|
|
phot_bp_mean_flux |
gaia_source |
GAIADR2 |
Integrated BP mean flux |
float |
8 |
electrons/s |
|
phot.flux;stat.mean |
phot_bp_mean_flux |
gaia_source |
GAIAEDR3 |
Integrated BP mean flux |
float |
8 |
electrons/s |
|
phot.flux;stat.mean |
phot_bp_mean_flux_error |
gaia_source |
GAIADR2 |
Standard error on the integrated BP mean flux |
float |
8 |
electrons/s |
|
stat.error;phot.flux;stat.mean |
phot_bp_mean_flux_error |
gaia_source |
GAIAEDR3 |
Standard error on the integrated BP mean flux |
real |
4 |
electrons/s |
|
stat.error;phot.flux;stat.mean |
phot_bp_mean_flux_over_error |
gaia_source |
GAIADR2 |
Integrated mean BP flux divided by its standard error |
real |
4 |
|
|
arith.ratio |
phot_bp_mean_flux_over_error |
gaia_source |
GAIAEDR3 |
Integrated mean BP flux divided by its standard error |
real |
4 |
|
|
arith.ratio |
phot_bp_mean_mag |
gaia_source |
GAIADR2 |
Integrated BP mean magnitude |
real |
4 |
mag |
|
phot.mag;stat.mean |
phot_bp_mean_mag |
gaia_source |
GAIAEDR3 |
Integrated BP mean magnitude |
real |
4 |
mag |
|
phot.mag;stat.mean |
phot_bp_n_blended_transits |
gaia_source |
GAIAEDR3 |
Number of BP blended transits |
smallint |
2 |
|
|
meta.number |
phot_bp_n_contaminated_transits |
gaia_source |
GAIAEDR3 |
Number of BP contaminated transits |
smallint |
2 |
|
|
meta.number |
phot_bp_n_obs |
gaia_source |
GAIADR2 |
Number of observations contributing to BP photometry |
int |
4 |
|
|
meta.number |
phot_bp_n_obs |
gaia_source |
GAIAEDR3 |
Number of observations contributing to BP photometry |
smallint |
2 |
|
|
meta.number |
phot_bp_rp_excess_factor |
gaia_source |
GAIADR2 |
Combined BP and RP excess factor |
real |
4 |
|
|
|
phot_bp_rp_excess_factor |
gaia_source |
GAIAEDR3 |
Combined BP and RP excess factor |
real |
4 |
|
|
arith.factor;phot.flux;em.opt |
phot_flag |
combo17CDFSSource |
COMBO17 |
flags on photometry: bit 0-7 (corresponding to values 0-128) are original SExtractor flags, bit 9-11 set by COMBO-17 photometry, bit 9 indicates only potential problem from bright neighbours or reflexes from the optics (check images), bit 10 indicates uncorrected hot pixels, bit 11 is set interactively when photometry is erroneous |
smallint |
2 |
|
|
|
phot_g_mean_flux |
gaia_source |
GAIADR2 |
G-band mean flux |
float |
8 |
electrons/s |
|
phot.flux;stat.mean;em.opt |
phot_g_mean_flux |
gaia_source |
GAIAEDR3 |
G-band mean flux |
float |
8 |
electrons/s |
|
phot.flux;stat.mean;em.opt |
phot_g_mean_flux |
gaia_source, tgas_source |
GAIADR1 |
G-band mean flux |
float |
8 |
electrons/s |
|
phot.flux;stat.mean;em.opt |
phot_g_mean_flux_error |
gaia_source |
GAIADR2 |
Error on G-band mean flux |
float |
8 |
electrons/s |
|
stat.error;phot.flux;stat.mean;em.opt |
phot_g_mean_flux_error |
gaia_source |
GAIAEDR3 |
Error on G-band mean flux |
real |
4 |
electrons/s |
|
stat.error;phot.flux;stat.mean;em.opt |
phot_g_mean_flux_error |
gaia_source, tgas_source |
GAIADR1 |
Error on G-band mean flux |
float |
8 |
electrons/s |
|
stat.error;phot.flux;stat.mean;em.opt |
phot_g_mean_flux_error_TGAS |
ravedr5Source |
RAVE |
Error on G-band mean flux from TGAS |
float |
8 |
e-/s |
|
stat.error;phot.flux;stat.mean;em.opt |
phot_g_mean_flux_over_error |
gaia_source |
GAIADR2 |
G-band mean flux divided by its standard error |
float |
8 |
|
|
arith.ratio |
phot_g_mean_flux_over_error |
gaia_source |
GAIAEDR3 |
G-band mean flux divided by its standard error |
real |
4 |
|
|
arith.ratio |
phot_g_mean_flux_TGAS |
ravedr5Source |
RAVE |
Error on G-band mean flux from TGAS |
float |
8 |
e-/s |
|
phot.flux;stat.mean;em.opt |
phot_g_mean_mag |
aux_qso_icrf2_match, gaia_source, tgas_source |
GAIADR1 |
G-band mean magnitude |
float |
8 |
mag |
|
phot.mag;stat.mean;em.opt |
phot_g_mean_mag |
gaia_source |
GAIADR2 |
G-band mean magnitude |
real |
4 |
mag |
|
phot.mag;stat.mean;em.opt |
phot_g_mean_mag |
gaia_source |
GAIAEDR3 |
G-band mean magnitude |
real |
4 |
mag |
|
phot.mag;stat.mean;em.opt |
phot_g_mean_mag_TGAS |
ravedr5Source |
RAVE |
G-band mean magnitude from TGAS |
float |
8 |
mag |
|
phot.mag;em.opt.g |
phot_g_n_obs |
gaia_source |
GAIADR2 |
Number of observations contributing to G band photometry |
int |
4 |
|
|
meta.number |
phot_g_n_obs |
gaia_source |
GAIAEDR3 |
Number of observations contributing to G band photometry |
smallint |
2 |
|
|
meta.number |
phot_g_n_obs |
gaia_source, tgas_source |
GAIADR1 |
Number of observations contributing to G band photometry |
int |
4 |
|
|
meta.number |
phot_proc_mode |
gaia_source |
GAIADR2 |
Photometry processing mode |
tinyint |
1 |
|
|
meta.code |
phot_proc_mode |
gaia_source |
GAIAEDR3 |
Photometry processing mode |
tinyint |
1 |
|
|
meta.code |
phot_rp_mean_flux |
gaia_source |
GAIADR2 |
Integrated RP mean flux |
float |
8 |
electrons/s |
|
phot.flux;stat.mean |
phot_rp_mean_flux |
gaia_source |
GAIAEDR3 |
Integrated RP mean flux |
float |
8 |
electrons/s |
|
phot.flux;stat.mean |
phot_rp_mean_flux_error |
gaia_source |
GAIADR2 |
Standard error on the integrated RP mean flux |
float |
8 |
electrons/s |
|
stat.error;phot.flux;stat.mean |
phot_rp_mean_flux_error |
gaia_source |
GAIAEDR3 |
Standard error on the integrated RP mean flux |
real |
4 |
electrons/s |
|
stat.error;phot.flux;stat.mean |
phot_rp_mean_flux_over_error |
gaia_source |
GAIADR2 |
Integrated mean RP flux divided by its standard error |
real |
4 |
|
|
arith.ratio |
phot_rp_mean_flux_over_error |
gaia_source |
GAIAEDR3 |
Integrated mean RP flux divided by its standard error |
real |
4 |
|
|
arith.ratio |
phot_rp_mean_mag |
gaia_source |
GAIADR2 |
Integrated RP mean magnitude |
real |
4 |
mag |
|
phot.mag;stat.mean |
phot_rp_mean_mag |
gaia_source |
GAIAEDR3 |
Integrated RP mean magnitude |
real |
4 |
mag |
|
phot.mag;stat.mean |
phot_rp_n_blended_transits |
gaia_source |
GAIAEDR3 |
Number of RP blended transits |
smallint |
2 |
|
|
meta.number |
phot_rp_n_contaminated_transits |
gaia_source |
GAIAEDR3 |
Number of RP contaminated transits |
smallint |
2 |
|
|
meta.number |
phot_rp_n_obs |
gaia_source |
GAIADR2 |
Number of observations contributing to RP photometry |
int |
4 |
|
|
meta.number |
phot_rp_n_obs |
gaia_source |
GAIAEDR3 |
Number of observations contributing to RP photometry |
smallint |
2 |
|
|
meta.number |
phot_variable_flag |
gaia_source |
GAIADR2 |
Photometric variability flag |
char |
16 |
|
|
meta.code;src.var |
phot_variable_flag |
gaia_source, tgas_source |
GAIADR1 |
Photometric variability flag |
varchar |
16 |
|
|
meta.code;src.var |
phot_variable_fundam_freq1 |
variable_summary |
GAIADR1 |
Fundamental frequency 1 |
float |
8 |
/days |
|
src.var.pulse |
photZPCat |
MultiframeDetector |
SHARKSv20210222 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
SHARKSv20210421 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
ULTRAVISTADR4 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSDR1 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSDR2 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSDR3 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSDR4 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSDR5 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSDR6 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20120926 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20130417 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20140409 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20150108 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20160114 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20160507 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20170630 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20180419 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20201209 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20231101 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VHSv20240731 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIDEODR2 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIDEODR3 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIDEODR4 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIDEODR5 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIDEOv20100513 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIDEOv20111208 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGDR2 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGDR3 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGDR4 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20110714 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20111019 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20130417 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20140402 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20150421 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20151230 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20160406 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20161202 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VIKINGv20170715 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCDEEPv20230713 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCDEEPv20240506 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCDR1 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCDR2 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCDR3 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCDR4 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCDR5 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20110816 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20110909 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20120126 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20121128 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20130304 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20130805 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20140428 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20140903 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20150309 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20151218 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20160311 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20160822 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20170109 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20170411 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20171101 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20180702 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20181120 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20191212 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20210708 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20230816 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VMCv20240226 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VVVDR1 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VVVDR2 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VVVDR5 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VVVXDR1 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VVVv20100531 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
MultiframeDetector |
VVVv20110718 |
Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct You can then make use of any of the assorted flux estimators to produce magnitudes via Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction. |
photZPCat |
PreviousMFDZP |
SHARKSv20210222 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
SHARKSv20210421 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
ULTRAVISTADR4 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSDR1 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSDR2 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSDR3 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSDR4 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSDR5 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSDR6 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20120926 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20130417 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20140409 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20150108 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20160114 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20160507 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20170630 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20180419 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20201209 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20231101 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VHSv20240731 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIDEODR2 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIDEODR3 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIDEODR4 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIDEODR5 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIDEOv20100513 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIDEOv20111208 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGDR2 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGDR3 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGDR4 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20110714 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20111019 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20130417 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20140402 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20150421 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20151230 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20160406 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20161202 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VIKINGv20170715 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCDEEPv20230713 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCDEEPv20240506 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCDR1 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCDR2 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCDR3 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCDR4 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCDR5 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20110816 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20110909 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20120126 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20121128 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20130304 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20130805 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20140428 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20140903 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20150309 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20151218 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20160311 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20160822 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20170109 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20170411 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20171101 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20180702 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20181120 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20191212 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20210708 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20230816 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VMCv20240226 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VVVDR1 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VVVDR2 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VVVDR5 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VVVXDR1 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VVVv20100531 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
PreviousMFDZP |
VVVv20110718 |
Photometric zeropoint for default extinction in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPCat |
sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector |
VSAQC |
Photometric zero point for default extinction for the catalogue data |
real |
4 |
mags |
-0.9999995e9 |
?? |
photZPErrCat |
MultiframeDetector |
SHARKSv20210222 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
SHARKSv20210421 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
ULTRAVISTADR4 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSDR1 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSDR2 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSDR3 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSDR4 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSDR5 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSDR6 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20120926 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20130417 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20140409 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20150108 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20160114 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20160507 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20170630 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20180419 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20201209 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20231101 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VHSv20240731 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIDEODR2 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIDEODR3 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIDEODR4 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIDEODR5 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIDEOv20100513 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} [Currently set to -1 for WFCAM data.] |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIDEOv20111208 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGDR2 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGDR3 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGDR4 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20110714 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20111019 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20130417 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20140402 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20150421 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20151230 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20160406 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20161202 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VIKINGv20170715 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCDEEPv20230713 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCDEEPv20240506 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCDR1 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCDR2 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCDR3 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCDR4 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCDR5 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20110816 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20110909 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20120126 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20121128 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20130304 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20130805 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20140428 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20140903 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20150309 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20151218 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20160311 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20160822 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20170109 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20170411 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20171101 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20180702 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20181120 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20191212 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20210708 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20230816 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VMCv20240226 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VVVDR1 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VVVDR2 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VVVDR5 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VVVXDR1 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VVVv20100531 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} [Currently set to -1 for WFCAM data.] |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
MultiframeDetector |
VVVv20110718 |
Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} |
real |
4 |
mags |
-0.9999995e9 |
?? |
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse. |
photZPErrCat |
PreviousMFDZP |
SHARKSv20210222 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
SHARKSv20210421 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
ULTRAVISTADR4 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSDR1 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSDR2 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSDR3 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSDR4 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSDR5 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSDR6 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20120926 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20130417 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20140409 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20150108 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20160114 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20160507 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20170630 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20180419 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20201209 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20231101 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VHSv20240731 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIDEODR2 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIDEODR3 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIDEODR4 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIDEODR5 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIDEOv20100513 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIDEOv20111208 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGDR2 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGDR3 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGDR4 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20110714 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20111019 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20130417 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20140402 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20150421 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20151230 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20160406 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20161202 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VIKINGv20170715 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCDEEPv20230713 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCDEEPv20240506 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCDR1 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCDR2 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCDR3 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCDR4 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCDR5 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20110816 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20110909 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20120126 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20121128 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20130304 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20130805 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20140428 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20140903 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20150309 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20151218 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20160311 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20160822 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20170109 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20170411 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20171101 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20180702 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20181120 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20191212 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20210708 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20230816 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VMCv20240226 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VVVDR1 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VVVDR2 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VVVDR5 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VVVXDR1 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VVVv20100531 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
PreviousMFDZP |
VVVv20110718 |
Photometric zeropoint error in catalogue header |
real |
4 |
mag |
-0.9999995e9 |
|
photZPErrCat |
sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector |
VSAQC |
Photometric zero point error for the catalogue data |
real |
4 |
mags |
-0.9999995e9 |
?? |
picoi |
Multiframe |
SHARKSv20210222 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
SHARKSv20210421 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
ULTRAVISTADR4 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSDR1 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSDR2 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSDR3 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSDR4 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSDR5 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSDR6 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20120926 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20130417 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20140409 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20150108 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20160114 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20160507 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20170630 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20180419 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20201209 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20231101 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VHSv20240731 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIDEODR2 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIDEODR3 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIDEODR4 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIDEODR5 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIDEOv20100513 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIDEOv20111208 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGDR2 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGDR3 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGDR4 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20110714 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20111019 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20130417 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20140402 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20150421 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20151230 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20160406 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20161202 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VIKINGv20170715 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCDEEPv20230713 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCDEEPv20240506 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCDR1 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCDR2 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCDR3 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCDR4 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCDR5 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20110816 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20110909 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20120126 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20121128 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20130304 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20130805 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20140428 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20140903 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20150309 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20151218 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20160311 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20160822 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20170109 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20170411 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20171101 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20180702 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20181120 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20191212 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20210708 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20230816 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VMCv20240226 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VVVDR1 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VVVDR2 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VVVDR5 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VVVXDR1 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VVVv20100531 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
Multiframe |
VVVv20110718 |
PI-COI name. {image primary HDU keyword: PI-COI} |
varchar |
64 |
|
NONE |
|
picoi |
sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe |
VSAQC |
PI-COI name. |
varchar |
64 |
|
NONE |
|
PID_R |
spectra |
SIXDF |
PID number read from R frame |
int |
4 |
|
|
|
PID_V |
spectra |
SIXDF |
PID number read from V frame |
int |
4 |
|
|
|
PIDL15 |
akari_lmc_psa_v1, akari_lmc_psc_v1 |
AKARI |
Observing Pointing identifier |
char |
9 |
|
9999999.9 |
|
PIDL24 |
akari_lmc_psa_v1, akari_lmc_psc_v1 |
AKARI |
Observing Pointing identifier |
char |
9 |
|
9999999.9 |
|
PIDN3 |
akari_lmc_psa_v1, akari_lmc_psc_v1 |
AKARI |
Observing Pointing identifier |
char |
9 |
|
9999999.9 |
|
PIDS11 |
akari_lmc_psa_v1, akari_lmc_psc_v1 |
AKARI |
Observing Pointing identifier |
char |
9 |
|
9999999.9 |
|
PIDS7 |
akari_lmc_psa_v1, akari_lmc_psc_v1 |
AKARI |
Observing Pointing identifier |
char |
9 |
|
9999999.9 |
|
PIVOT_R |
spectra |
SIXDF |
R pivot number |
smallint |
2 |
|
|
|
PIVOT_V |
spectra |
SIXDF |
V pivot number |
smallint |
2 |
|
|
|
Pix_x_I |
denisDR3Source |
DENIS |
Pixel x position in I band |
float |
8 |
pix |
|
|
Pix_x_J |
denisDR3Source |
DENIS |
Pixel x position in J band |
float |
8 |
pix |
|
|
Pix_x_K |
denisDR3Source |
DENIS |
Pixel x position in K band |
float |
8 |
pix |
|
|
Pix_y_I |
denisDR3Source |
DENIS |
Pixel y position in I band |
float |
8 |
pix |
|
|
Pix_y_J |
denisDR3Source |
DENIS |
Pixel y position in J band |
float |
8 |
pix |
|
|
Pix_y_K |
denisDR3Source |
DENIS |
Pixel y position in K band |
float |
8 |
pix |
|
|
pixelID |
vvvBulge3DExtinctVals |
EXTINCT |
UID of the pixel |
int |
4 |
|
|
meta.id |
pixelID |
vvvBulgeExtMapCoords |
EXTINCT |
UID of the 2D pixel |
int |
4 |
|
|
meta.id;meta.main |
pixelSize |
RequiredMosaic |
SHARKSv20210222 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
SHARKSv20210421 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
ULTRAVISTADR4 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSDR1 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSDR2 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSDR3 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSDR4 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSDR5 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSDR6 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20120926 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20130417 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20150108 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20160114 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20160507 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20170630 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20180419 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20201209 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20231101 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VHSv20240731 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIDEODR2 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIDEODR3 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIDEODR4 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIDEODR5 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIDEOv20100513 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIDEOv20111208 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGDR2 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGDR3 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGDR4 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20110714 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20111019 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20130417 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20150421 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20151230 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20160406 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20161202 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VIKINGv20170715 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCDEEPv20230713 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCDEEPv20240506 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCDR1 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCDR3 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCDR4 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCDR5 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20110816 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20110909 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20120126 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20121128 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20130304 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20130805 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20140428 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20140903 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20150309 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20151218 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20160311 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20160822 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20170109 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20170411 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20171101 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20180702 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20181120 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20191212 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20210708 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20230816 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VMCv20240226 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VVVDR1 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VVVDR2 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VVVDR5 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VVVXDR1 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VVVv20100531 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaic |
VVVv20110718 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
|
?? |
pixelSize |
RequiredMosaicTopLevel |
SHARKSv20210222 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
SHARKSv20210421 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
ULTRAVISTADR4 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VHSv20201209 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VHSv20231101 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VHSv20240731 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VMCDEEPv20230713 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VMCDEEPv20240506 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VMCDR5 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VMCv20191212 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VMCv20210708 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VMCv20230816 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VMCv20240226 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VVVDR5 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixelSize |
RequiredMosaicTopLevel |
VVVXDR1 |
The final pixel size of the mosaic |
real |
4 |
arcsec |
-0.9999995e9 |
?? |
pixSizeAng |
ThreeDimExtinctionMaps |
EXTINCT |
Angular resolution of extinction map |
real |
4 |
Arcminutes |
-0.9999995e9 |
|
pixSizeRad |
ThreeDimExtinctionMaps |
EXTINCT |
Radial resolution of extinction map |
real |
4 |
kpc |
-0.9999995e9 |
|
pJKs |
vvvPsfDaophotJKsSource |
VVVDR5 |
The fraction of the number of "recovered" vs injected stars per (J-Ks) - Ks bin {catalogue TType keyword: p} |
real |
4 |
|
-0.9999995e9 |
|
PlateNumber |
ravedr5Source |
RAVE |
Number of fieldplate on instrument [1..3] |
tinyint |
1 |
|
|
meta.id;instr.plate |
pltScale |
Detection |
PS1DR2 |
Local plate scale at this location. |
real |
4 |
arcsec/pixel |
-999 |
|
plx |
hipparcos_new_reduction |
GAIADR1 |
Parallax |
float |
8 |
milliarcseconds |
|
pos.parallax |
plx |
vvvParallaxCatalogue |
VVVDR5 |
Parallax. These are inverse variance weighted averages across their measured values in both equatorial tangent plane dimensions and from all pawprint sets. {catalogue TType keyword: plx} |
float |
8 |
mas |
-999999500.0 |
|
pm |
gaia_source |
GAIAEDR3 |
Total proper motion |
real |
4 |
milliarcsec/year |
|
pos.pm;pos.eq |
pm |
vvvParallaxCatalogue, vvvProperMotionCatalogue |
VVVDR5 |
Total Proper motion {catalogue TType keyword: pm} |
float |
8 |
mas/yr |
-999999500.0 |
|
pm_de |
hipparcos_new_reduction |
GAIADR1 |
Proper motion in Declination |
float |
8 |
milliarcseconds/year |
|
pos.eq.dec;pos.pm |
pm_de |
tycho2 |
GAIADR1 |
Proper motion in Dec |
real |
4 |
milliarcsec/year |
|
pos.eq.dec;pos.pm |
pm_dec |
igsl_source |
GAIADR1 |
Proper motion in Dec at catalogue epoch |
real |
4 |
milliarcsec/year |
|
pos.pm;pos.eq.dec |
pm_dec_error |
igsl_source |
GAIADR1 |
Error in proper motion in Dec |
real |
4 |
milliarcsec/year |
|
stat.error;pos.pm;pos.eq.dec |
pm_ra |
hipparcos_new_reduction |
GAIADR1 |
Proper motion in Right Ascension |
float |
8 |
milliarcseconds/year |
|
pos.eq.ra;pos.pm |
pm_ra |
igsl_source |
GAIADR1 |
Proper motion in RA at catalogue epoch |
real |
4 |
milliarcsec/year |
|
pos.pm;pos.eq.ra |
pm_ra |
tycho2 |
GAIADR1 |
Proper motion in RA*cos(Dec) |
real |
4 |
milliarcsec/year |
|
pos.eq.ra;pos.pm |
pm_ra_error |
igsl_source |
GAIADR1 |
Error in proper motion in RA |
real |
4 |
milliarcsec/year |
|
stat.error;pos.pm;pos.eq.ra |
PMAG |
grs_ngpSource, grs_ranSource, grs_sgpSource |
TWODFGRS |
Unmatched raw APM profile integrated mag |
real |
4 |
|
|
|
pmcode |
allwise_sc |
WISE |
This is a five character string that encodes information about factors that impact the accuracy of the motion estimation. These include the original blend count, whether a blend-swap took place, and the distance in hundredths of an arcsecond between the non-motion position and the motion mean-observation-epoch position. This column is null if a motion solution was not attempted or a valid solution was not found. |
varchar |
5 |
|
|
|
The format is NQDDD where N is the original blend count, Q is either "Y" or "N" for "yes" or "no" a blend-swap occurred (i.e., the original primary component was not the final primary component), and DDD is the radial distance between the non-motion and motion at mean-observation epoch positions in units of 0.01 arcsec, clipped at 999 (almost 10 arcsec). For example, a well-behaved source that is not part of a blend and that has similar stationary and motion fit positions would have a pmcode value like "1N008". A source with a questionable motion estimate that is passively deblended (nb=2) and whose stationary-fit and motion position differ by a significant amount would have a pmcode value like "3Y234". |
pmcode |
catwise_2020, catwise_prelim |
WISE |
quality of the PM solution |
varchar |
5 |
|
|
|
pmDE_error_TGAS |
ravedr5Source |
RAVE |
Error of proper motion (DE) |
float |
8 |
mas/yr |
|
stat.error;pos.pm;pos.eq.dec |
pmDE_PPMXL |
ravedr5Source |
RAVE |
Proper Motion (Declination) |
real |
4 |
mas/yr |
|
pos.pm |
pmDE_TGAS |
ravedr5Source |
RAVE |
Proper motion (Declination) |
float |
8 |
mas/yr |
|
pos.pm;pos.eq.dec |
pmDE_TYCHO2 |
ravedr5Source |
RAVE |
Proper motion (Declination) |
real |
4 |
mas/yr |
|
pos.pm;pos.eq.dec |
pmDE_UCAC4 |
ravedr5Source |
RAVE |
Proper Motion (Declination) |
real |
4 |
mas/yr |
|
pos.pm |
pmDE_USNOB1 |
ravedr5Source |
RAVE |
Proper Motion (Declination) |
real |
4 |
mas/yr |
|
pos.pm |
PMDec |
catwise_2020, catwise_prelim |
WISE |
proper motion in dec |
real |
4 |
arcsec/yr |
|
|
pmDec |
ukirtFSstars |
VIDEOv20100513 |
Proper motion in Dec |
real |
4 |
arcsec per year |
0.0 |
|
pmDec |
ukirtFSstars |
VIKINGv20110714 |
Proper motion in Dec |
real |
4 |
arcsec per year |
0.0 |
|
pmDec |
ukirtFSstars |
VVVv20100531 |
Proper motion in Dec |
real |
4 |
arcsec per year |
0.0 |
|
pmdec |
allwise_sc |
WISE |
The apparent motion in declination estimated for this source. This column is null if the motion fit failed to converge or was not attempted. CAUTION: This is the total motion in declination, and not the proper motion. The apparent motion can be significantly affected by parallax for nearby objects. |
int |
4 |
mas/year |
|
|
pmdec |
gaia_source |
GAIADR2 |
Proper motion in Declination direction |
float |
8 |
milliarcsec/year |
|
pos.pm;.pos.eq.dec |
pmdec |
gaia_source |
GAIAEDR3 |
Proper motion in Declination direction |
float |
8 |
milliarcsec/year |
|
pos.pm;.pos.eq.dec |
pmdec |
gaia_source, tgas_source |
GAIADR1 |
Proper motion in Declination direction |
float |
8 |
milliarcsec/year |
|
pos.pm;.pos.eq.dec |
pmdec_error |
gaia_source |
GAIADR2 |
Error of proper motion in Declination direction |
float |
8 |
milliarcsec/year |
|
stat.error;pos.pm;.pos.eq.dec |
pmdec_error |
gaia_source |
GAIAEDR3 |
Error of proper motion in Declination direction |
real |
4 |
milliarcsec/year |
|
stat.error;pos.pm;.pos.eq.dec |
pmdec_error |
gaia_source, tgas_source |
GAIADR1 |
Error of proper motion in Declination direction |
float |
8 |
milliarcsec/year |
|
stat.error;pos.pm;.pos.eq.dec |
pmdec_pseudocolour_corr |
gaia_source |
GAIAEDR3 |
Correlation between proper motion in declination and pseudocolour |
real |
4 |
|
|
stat.correlation;em.wavenumber;pos.pm;pos.eq.dec |
pmEta |
vmcProperMotionCatalogue |
VMCv20240226 |
PM in Eta direction {catalogue TType keyword: pmeta} |
float |
8 |
mas/yr |
-999999500.0 |
|
pmID |
vmcProperMotionCatalogue |
VMCv20240226 |
UID of star in the proper motion catalogue |
bigint |
8 |
|
|
meta.id;meta.main |
PMRA |
catwise_2020, catwise_prelim |
WISE |
motion in ra |
real |
4 |
arcsec/yr |
|
|
pmRA |
ukirtFSstars |
VIDEOv20100513 |
Proper motion in RA |
real |
4 |
arcsec per year |
0.0 |
|
pmRA |
ukirtFSstars |
VIKINGv20110714 |
Proper motion in RA |
real |
4 |
arcsec per year |
0.0 |
|
pmRA |
ukirtFSstars |
VVVv20100531 |
Proper motion in RA |
real |
4 |
arcsec per year |
0.0 |
|
pmra |
allwise_sc |
WISE |
The apparent motion in right ascension estimated for this source. This column is null if the motion fit failed to converge or was not attempted. CAUTION: This is the total motion in right ascension, and not the proper motion. The apparent motion can be significantly affected by parallax for nearby objects. |
int |
4 |
mas/year |
|
|
pmra |
gaia_source |
GAIADR2 |
Proper motion in Right Ascension direction |
float |
8 |
milliarcsec/year |
|
pos.pm;.pos.eq.ra |
pmra |
gaia_source |
GAIAEDR3 |
Proper motion in Right Ascension direction |
float |
8 |
milliarcsec/year |
|
pos.pm;.pos.eq.ra |
pmra |
gaia_source, tgas_source |
GAIADR1 |
Proper motion in Right Ascension direction |
float |
8 |
milliarcsec/year |
|
pos.pm;.pos.eq.ra |
pmra_error |
gaia_source |
GAIADR2 |
Error of proper motion in Right Ascension direction |
float |
8 |
milliarcsec/year |
|
stat.error;pos.pm;.pos.eq.ra |
pmra_error |
gaia_source |
GAIAEDR3 |
Error of proper motion in Right Ascension direction |
real |
4 |
milliarcsec/year |
|
stat.error;pos.pm;.pos.eq.ra |
pmra_error |
gaia_source, tgas_source |
GAIADR1 |
Error of proper motion in Right Ascension direction |
float |
8 |
milliarcsec/year |
|
stat.error;pos.pm;.pos.eq.ra |
pmRA_error_TGAS |
ravedr5Source |
RAVE |
Error of proper motion (RA) |
float |
8 |
mas/yr |
|
stat.errror;pos.pm;pos.eq.ra |
pmra_pmdec_corr |
gaia_source |
GAIADR2 |
Correlation between proper motion in Right Ascension and proper motion in Declination |
real |
4 |
|
|
stat.correlation;pos.pm;pos.eq.ra;pos.pm;pos.eq.dec |
pmra_pmdec_corr |
gaia_source |
GAIAEDR3 |
Correlation between proper motion in Right Ascension and proper motion in Declination |
real |
4 |
|
|
stat.correlation;pos.pm;pos.eq.ra;pos.pm;pos.eq.dec |
pmra_pmdec_corr |
gaia_source, tgas_source |
GAIADR1 |
Correlation between proper motion in Right Ascension and proper motion in Declination |
real |
4 |
|
|
stat.correlation |
pmRA_PPMXL |
ravedr5Source |
RAVE |
Proper Motion (Right Ascension) |
real |
4 |
mas/yr |
|
pos.pm;pos.eq.ra |
pmra_pseudocolour_corr |
gaia_source |
GAIAEDR3 |
Correlation between proper motion in right ascension and pseudocolour |
real |
4 |
|
|
stat.correlation;em.wavenumber;pos.pm;pos.eq.ra |
pmRA_TGAS |
ravedr5Source |
RAVE |
Proper motion (Right Ascension) |
float |
8 |
mas/yr |
|
pos.pm;pos.eq.ra |
pmRA_TYCHO2 |
ravedr5Source |
RAVE |
Proper Motion (Right Ascension) |
real |
4 |
mas/yr |
|
pos.pm;pos.eq.ra |
pmRA_UCAC4 |
ravedr5Source |
RAVE |
Proper Motion (Right Ascension) |
real |
4 |
mas/yr |
|
pos.pm;pos.eq.ra |
pmRA_USNOB1 |
ravedr5Source |
RAVE |
Proper Motion (Right Ascension) |
real |
4 |
mas/yr |
|
pos.pm;pos.eq.ra |
pmXi |
vmcProperMotionCatalogue |
VMCv20240226 |
PM in Xi direction {catalogue TType keyword: pmxi} |
float |
8 |
mas/yr |
-999999500.0 |
|
PN_1_BG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 1 background map. Made using a 12 x 12 nodes spline fit on the source-free individual-band images. |
real |
4 |
counts/pixel |
|
|
PN_1_DET_ML |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 1 Maximum likelihood |
real |
4 |
|
|
|
PN_1_EXP |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 1 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. |
real |
4 |
s |
|
|
PN_1_FLUX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 1 flux |
real |
4 |
erg/cm**2/s |
|
|
PN_1_FLUX_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 1 flux error |
real |
4 |
erg/cm**2/s |
|
|
PN_1_RATE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 1 Count rates |
real |
4 |
counts/s |
|
|
PN_1_RATE_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 1 Count rates error |
real |
4 |
counts/s |
|
|
PN_1_VIG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 1 vignetting value. |
real |
4 |
|
|
|
PN_2_BG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 2 background map. Made using a 12 x 12 nodes spline fit on the source-free individual-band images. |
real |
4 |
counts/pixel |
|
|
PN_2_DET_ML |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 2 Maximum likelihood |
real |
4 |
|
|
|
PN_2_EXP |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 2 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. |
real |
4 |
s |
|
|
PN_2_FLUX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 2 flux |
real |
4 |
erg/cm**2/s |
|
|
PN_2_FLUX_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 2 flux error |
real |
4 |
erg/cm**2/s |
|
|
PN_2_RATE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 2 Count rates |
real |
4 |
counts/s |
|
|
PN_2_RATE_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 2 Count rates error |
real |
4 |
counts/s |
|
|
PN_2_VIG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 2 vignetting value. |
real |
4 |
|
|
|
PN_3_BG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 3 background map. Made using a 12 x 12 nodes spline fit on the source-free individual-band images. |
real |
4 |
counts/pixel |
|
|
PN_3_DET_ML |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 3 Maximum likelihood |
real |
4 |
|
|
|
PN_3_EXP |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 3 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. |
real |
4 |
s |
|
|
PN_3_FLUX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 3 flux |
real |
4 |
erg/cm**2/s |
|
|
PN_3_FLUX_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 3 flux error |
real |
4 |
erg/cm**2/s |
|
|
PN_3_RATE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 3 Count rates |
real |
4 |
counts/s |
|
|
PN_3_RATE_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 3 Count rates error |
real |
4 |
counts/s |
|
|
PN_3_VIG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 3 vignetting value. |
real |
4 |
|
|
|
PN_4_BG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 4 background map. Made using a 12 x 12 nodes spline fit on the source-free individual-band images. |
real |
4 |
counts/pixel |
|
|
PN_4_DET_ML |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 4 Maximum likelihood |
real |
4 |
|
|
|
PN_4_EXP |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 4 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. |
real |
4 |
s |
|
|
PN_4_FLUX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 4 flux |
real |
4 |
erg/cm**2/s |
|
|
PN_4_FLUX_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 4 flux error |
real |
4 |
erg/cm**2/s |
|
|
PN_4_RATE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 4 Count rates |
real |
4 |
counts/s |
|
|
PN_4_RATE_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 4 Count rates error |
real |
4 |
counts/s |
|
|
PN_4_VIG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 4 vignetting value. |
real |
4 |
|
|
|
PN_5_BG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 5 background map. Made using a 12 x 12 nodes spline fit on the source-free individual-band images. |
real |
4 |
counts/pixel |
|
|
PN_5_DET_ML |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 5 Maximum likelihood |
real |
4 |
|
|
|
PN_5_EXP |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 5 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. |
real |
4 |
s |
|
|
PN_5_FLUX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 5 flux |
real |
4 |
erg/cm**2/s |
|
|
PN_5_FLUX_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 5 flux error |
real |
4 |
erg/cm**2/s |
|
|
PN_5_RATE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 5 Count rates |
real |
4 |
counts/s |
|
|
PN_5_RATE_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 5 Count rates error |
real |
4 |
counts/s |
|
|
PN_5_VIG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN band 5 vignetting value. |
real |
4 |
|
|
|
PN_8_CTS |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
Combined band source counts |
real |
4 |
counts |
|
|
PN_8_CTS_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
Combined band source counts 1 σ error |
real |
4 |
counts |
|
|
PN_8_DET_ML |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 8 Maximum likelihood |
real |
4 |
|
|
|
PN_8_FLUX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 8 flux |
real |
4 |
erg/cm**2/s |
|
|
PN_8_FLUX_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 8 flux error |
real |
4 |
erg/cm**2/s |
|
|
PN_8_RATE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 8 Count rates |
real |
4 |
counts/s |
|
|
PN_8_RATE_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 8 Count rates error |
real |
4 |
counts/s |
|
|
PN_9_DET_ML |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 9 Maximum likelihood |
real |
4 |
|
|
|
PN_9_FLUX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 9 flux |
real |
4 |
erg/cm**2/s |
|
|
PN_9_FLUX_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 9 flux error |
real |
4 |
erg/cm**2/s |
|
|
PN_9_RATE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 9 Count rates |
real |
4 |
counts/s |
|
|
PN_9_RATE_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
PN band 9 Count rates error |
real |
4 |
counts/s |
|
|
PN_CHI2PROB |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 |
XMM |
The Chi² probability (based on the null hypothesis) that the source as detected by the PN camera is constant. The Pearson approximation to Chi² for Poissonian data was used, in which the model is used as the estimator of its own variance . If more than one exposure (that is, time series) is available for this source the smallest value of probability was used. |
real |
4 |
|
|
|
PN_CHI2PROB |
xmm3dr4 |
XMM |
The Chi² probability (based on the null hypothesis) that the source as detected by the PN camera is constant. The Pearson approximation to Chi² for Poissonian data was used, in which the model is used as the estimator of its own variance . If more than one exposure (that is, time series) is available for this source the smallest value of probability was used. |
float |
8 |
|
|
|
PN_FILTER |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 |
XMM |
PN filter. The options are Thick, Medium, Thin1, Thin2, and Open, depending on the efficiency of the optical blocking. |
varchar |
6 |
|
|
|
PN_FILTER |
xmm3dr4 |
XMM |
PN filter. The options are Thick, Medium, Thin1, Thin2, and Open, depending on the efficiency of the optical blocking. |
varchar |
50 |
|
|
|
PN_FLAG |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 |
XMM |
PN flag string made of the flags 1 - 12 (counted from left to right) for the PN source detection. In case where the camera was not used in the source detection a dash is given. In case a source was not detected by the PN the flags are all set to False (default). Flag 10 is not used. |
varchar |
12 |
|
|
|
PN_FLAG |
xmm3dr4 |
XMM |
PN flag string made of the flags 1 - 12 (counted from left to right) for the PN source detection. In case where the camera was not used in the source detection a dash is given. In case a source was not detected by the PN the flags are all set to False (default). Flag 10 is not used. |
varchar |
50 |
|
|
|
PN_FVAR |
xmm3dr4 |
XMM |
The fractional excess variance measured in the PN timeseries of the detection. Where multiple PN exposures exist, it is for the one giving the largest probability of variability (PN_CHI2PROB). This quantity provides a measure of the amplitude of variability in the timeseries, above purely statistical fluctuations. |
float |
8 |
|
|
|
PN_FVARERR |
xmm3dr4 |
XMM |
The error on the fractional excess variance for the PN timeseries of the detection (PN_FVAR). |
float |
8 |
|
|
|
PN_HR1 |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN hardness ratio between the bands 1 & 2 In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. |
real |
4 |
|
|
|
PN_HR1_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The 1 σ error of the PN hardness ratio between the bands 1 & 2 |
real |
4 |
|
|
|
PN_HR2 |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN hardness ratio between the bands 2 & 3 In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. |
real |
4 |
|
|
|
PN_HR2_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The 1 σ error of the PN hardness ratio between the bands 2 & 3 |
real |
4 |
|
|
|
PN_HR3 |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN hardness ratio between the bands 3 & 4 In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. |
real |
4 |
|
|
|
PN_HR3_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The 1 σ error of the PN hardness ratio between the bands 3 & 4 |
real |
4 |
|
|
|
PN_HR4 |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN hardness ratio between the bands 4 & 5 In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. |
real |
4 |
|
|
|
PN_HR4_ERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The 1 σ error of the PN hardness ratio between the bands 4 & 5 |
real |
4 |
|
|
|
PN_MASKFRAC |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PSF weighted mean of the detector coverage of a detection as derived from the detection mask. Sources which have less than 0.15 of their PSF covered by the detector are considered as being not detected. |
real |
4 |
|
|
|
PN_OFFAX |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN offaxis angle (the distance between the detection position and the onaxis position on the respective detector). The offaxis angle for a camera can be larger than 15 arcminutes when the detection is located outside the FOV of that camera. |
real |
4 |
arcmin |
|
|
PN_ONTIME |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
The PN ontime value (the total good exposure time (after GTI filtering) of the CCD where the detection is positioned). If a source position falls into CCD gaps or outside of the detector it will have a NULL given. |
real |
4 |
s |
|
|
PN_SUBMODE |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 |
XMM |
PN observing mode. The options are full frame mode with the full FOV exposed (in two sub-modes), and large window mode with only parts of the FOV exposed. |
varchar |
23 |
|
|
|
PN_SUBMODE |
xmm3dr4 |
XMM |
PN observing mode. The options are full frame mode with the full FOV exposed (in two sub-modes), and large window mode with only parts of the FOV exposed. |
varchar |
50 |
|
|
|
pNearH |
iras_psc |
IRAS |
Number of nearby hours-confirmed point sources |
tinyint |
1 |
|
|
meta.number |
pNearW |
iras_psc |
IRAS |
Number of nearby weeks-confirmed point sources |
tinyint |
1 |
|
|
meta.number |
pNoise |
sharksSource |
SHARKSv20210222 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
sharksSource |
SHARKSv20210421 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
ultravistaSource, ultravistaSourceRemeasurement |
ULTRAVISTADR4 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSDR1 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSDR2 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSDR3 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSDR4 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSDR5 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSDR6 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20120926 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20130417 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20140409 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20150108 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20160114 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20160507 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20170630 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20180419 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vhsSource |
VHSv20201209 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 |
VHSv20231101 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 |
VHSv20240731 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
videoSource |
VIDEODR2 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
videoSource |
VIDEODR3 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
videoSource |
VIDEODR4 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
videoSource |
VIDEODR5 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
videoSource |
VIDEOv20100513 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
videoSource |
VIDEOv20111208 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGDR2 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGDR3 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGDR4 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20110714 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20111019 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20130417 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20140402 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20150421 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20151230 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20160406 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20161202 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingSource |
VIKINGv20170715 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20160909 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20170124 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCDR2 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCDR3 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCDR4 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCDR5 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20110816 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20110909 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20120126 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20121128 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20130304 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20130805 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20140428 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20140903 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20150309 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20151218 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20160311 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20160822 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20170109 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20170411 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20171101 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20180702 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20181120 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20191212 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20210708 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20230816 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource |
VMCv20240226 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcSource, vmcSynopticSource |
VMCDR1 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcdeepSource |
VMCDEEPv20240506 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vmcdeepSource, vmcdeepSynopticSource |
VMCDEEPv20230713 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vvvSource |
VVVDR2 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vvvSource |
VVVDR5 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vvvSource |
VVVv20100531 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vvvSource |
VVVv20110718 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pNoise |
vvvSource, vvvSynopticSource |
VVVDR1 |
Probability that the source is noise |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 |
vvvxSource |
VVVXDR1 |
Probability that the source is noise |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pOB |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being a star of type O or B. {catalogue TType keyword: OB} |
float |
8 |
|
|
|
pOBErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being a star of type O or B. {catalogue TType keyword: OB_err} |
float |
8 |
|
|
|
polFlux |
nvssSource |
NVSS |
Integrated linearly polarized flux density |
real |
4 |
mJy |
|
PHOT_FLUX_LINEAR |
polPA |
nvssSource |
NVSS |
[-90,90] The position angle of polFlux |
real |
4 |
degress |
|
POS_POS-EQ |
pos |
iras_asc |
IRAS |
Position Angle from IRAS Source to Association (E of N) |
smallint |
2 |
degrees |
|
pos.posAng |
posAng |
iras_psc |
IRAS |
Uncertainty ellipse position angle (East of North) |
smallint |
2 |
degrees |
|
pos.posAng |
posAngle |
CurrentAstrometry |
SHARKSv20210222 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
SHARKSv20210421 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
ULTRAVISTADR4 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSDR1 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSDR2 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSDR3 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSDR4 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSDR5 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSDR6 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20120926 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20130417 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20140409 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20150108 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20160114 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20160507 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20170630 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20180419 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20201209 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20231101 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VHSv20240731 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIDEODR2 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIDEODR3 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIDEODR4 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIDEODR5 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIDEOv20100513 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIDEOv20111208 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGDR2 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGDR3 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGDR4 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20110714 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20111019 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20130417 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20140402 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20150421 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20151230 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20160406 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20161202 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VIKINGv20170715 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCDEEPv20230713 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCDEEPv20240506 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCDR1 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCDR2 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCDR3 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCDR4 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCDR5 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20110816 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20110909 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20120126 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20121128 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20130304 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20130805 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20140428 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20140903 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20150309 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20151218 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20160311 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20160822 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20170109 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20170411 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20171101 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20180702 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20181120 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20191212 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20210708 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20230816 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VMCv20240226 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VVVDR1 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VVVDR2 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VVVDR5 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VVVXDR1 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VVVv20100531 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
CurrentAstrometry |
VVVv20110718 |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngle |
Detection |
PS1DR2 |
Position angle (sky-to-chip) at this location. |
real |
4 |
degrees |
-999 |
|
posAngle |
RequiredMosaic |
SHARKSv20210421 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
ULTRAVISTADR4 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSDR1 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSDR2 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSDR3 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSDR4 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSDR5 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSDR6 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20120926 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20130417 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20150108 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20160114 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20160507 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20170630 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20180419 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20201209 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20231101 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VHSv20240731 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIDEODR2 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIDEODR3 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIDEODR4 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIDEODR5 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIDEOv20111208 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGDR2 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGDR3 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGDR4 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20110714 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20111019 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20130417 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20150421 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20151230 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20160406 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20161202 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VIKINGv20170715 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCDEEPv20230713 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCDEEPv20240506 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCDR1 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCDR3 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCDR4 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCDR5 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20110816 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20110909 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20120126 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20121128 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20130304 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20130805 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20140428 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20140903 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20150309 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20151218 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20160311 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20160822 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20170109 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20170411 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20171101 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20180702 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20181120 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20191212 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20210708 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20230816 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VMCv20240226 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VVVDR1 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VVVDR2 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VVVDR5 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VVVXDR1 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic |
VVVv20110718 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
RequiredMosaic, RequiredRegion, RequiredStack, RequiredTile |
SHARKSv20210222 |
Orientation of image x-axis to N-S |
real |
4 |
deg |
-0.9999995e9 |
|
posAngle |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
orientation of image x-axis to N-S |
float |
8 |
Degrees |
-0.9999995e9 |
pos.posAng |
posAngleTolerance |
Programme |
SHARKSv20210222 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
SHARKSv20210421 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
ULTRAVISTADR4 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSDR1 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSDR2 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSDR3 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSDR4 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSDR5 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSDR6 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20120926 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20130417 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20150108 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20160114 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20160507 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20170630 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20180419 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20201209 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20231101 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VHSv20240731 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIDEODR2 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIDEODR3 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIDEODR4 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIDEODR5 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIDEOv20111208 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGDR2 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGDR3 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGDR4 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20110714 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20111019 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20130417 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20150421 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20151230 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20160406 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20161202 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VIKINGv20170715 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCDEEPv20230713 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCDEEPv20240506 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCDR1 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCDR3 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCDR4 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCDR5 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20110816 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20110909 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20120126 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20121128 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20130304 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20130805 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20140428 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20140903 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20150309 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20151218 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20160311 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20160822 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20170109 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20170411 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20171101 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20180702 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20181120 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20191212 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20210708 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20230816 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VMCv20240226 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VSAQC |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VVVDR1 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VVVDR2 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VVVDR5 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VVVXDR1 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
posAngleTolerance |
Programme |
VVVv20110718 |
The position angle tolerance used when creating deep stacks and tiles |
real |
4 |
Degrees |
-0.9999995e9 |
?? |
POSCOROK |
xmm3dr4 |
XMM |
Signifies whether catcorr obtained a statistically reliable solution or not (0 = False, 1 = True). This parameter is redundant in the sense that if REFCAT is positive, then a reliable solution was considered to have been found. |
bit |
1 |
|
|
|
POSERR |
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 |
XMM |
Total position uncertainty in arcseconds calculated by combining the statistical error RADEC_ERR and the systematic error SYSERR as follows: POSERR = SQRT ( RADEC_ERR² + SYSERR² ). |
real |
4 |
arcsec |
|
|
posflg |
tycho2 |
GAIADR1 |
Type of Tycho2 solution |
varchar |
1 |
|
|
meta.id;stat.fit |
posMeanChisq |
ObjectThin |
PS1DR2 |
Reduced chi squared value of mean position. |
real |
4 |
|
-999 |
stat.stdev |
posstdev |
decapsSource |
DECAPS |
Standard deviation in position of object between different detections {catalogue TType keyword: posstdev} |
real |
4 |
arcsec |
|
stat.error;pos.eq |
posstdev_ok |
decapsSource |
DECAPS |
Standard deviation in position of object between different detections {catalogue TType keyword: posstdev_ok} |
real |
4 |
arcsec |
|
stat.error;pos.eq |
ppErrBits |
sharksDetection |
SHARKSv20210222 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
sharksDetection |
SHARKSv20210421 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
ultravistaDetection, ultravistaMapRemeasurement |
ULTRAVISTADR4 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
ultravistaMapRemeasAver |
ULTRAVISTADR4 |
additional WFAU post-processing error bits based on combining average pawprint and tile flagging |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSDR1 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSDR2 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSDR3 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSDR4 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSDR5 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSDR6 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20120926 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20130417 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20140409 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20150108 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20160114 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20160507 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20170630 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20180419 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20201209 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20231101 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsDetection |
VHSv20240731 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vhsListRemeasurement |
VHSDR1 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBits |
videoDetection |
VIDEODR2 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
videoDetection |
VIDEODR3 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
videoDetection |
VIDEODR4 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
videoDetection |
VIDEODR5 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
videoDetection |
VIDEOv20100513 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
videoDetection |
VIDEOv20111208 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
videoListRemeasurement |
VIDEOv20100513 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBits |
vikingDetection |
VIKINGDR2 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGDR3 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGDR4 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20110714 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20111019 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20130417 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20140402 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20150421 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20151230 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20160406 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20161202 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingDetection |
VIKINGv20170715 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingListRemeasurement |
VIKINGv20110714 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBits |
vikingListRemeasurement |
VIKINGv20111019 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBits |
vikingMapRemeasAver |
VIKINGZYSELJv20160909 |
additional WFAU post-processing error bits based on combining average pawprint and tile flagging |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingMapRemeasAver |
VIKINGZYSELJv20170124 |
additional WFAU post-processing error bits based on combining average pawprint and tile flagging |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingMapRemeasurement |
VIKINGZYSELJv20160909 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vikingMapRemeasurement |
VIKINGZYSELJv20170124 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCDR1 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCDR2 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCDR3 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCDR4 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCDR5 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20110816 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20110909 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20120126 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20121128 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20130304 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20130805 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20140428 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20140903 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20150309 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20151218 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20160311 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20160822 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20170109 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20170411 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20171101 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20180702 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20181120 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20191212 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20210708 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20230816 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcDetection |
VMCv20240226 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcListRemeasurement |
VMCv20110816 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBits |
vmcListRemeasurement |
VMCv20110909 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBits |
vmcdeepDetection |
VMCDEEPv20230713 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vmcdeepDetection |
VMCDEEPv20240506 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vvvDetection |
VVVDR1 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vvvDetection |
VVVDR2 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vvvDetection |
VVVv20100531 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings: Byte | Bit | Detection quality issue | Threshold or bit mask | Applies to | | | | Decimal | Hexadecimal | | 0 | 4 | Deblended | 16 | 0x00000010 | All VDFS catalogues | 0 | 6 | Bad pixel(s) in default aperture | 64 | 0x00000040 | All VDFS catalogues | 0 | 7 | Low confidence in default aperture | 128 | 0x00000080 | All VDFS catalogues | 1 | 12 | Lies within detector 16 region of a tile | 4096 | 0x00001000 | All catalogues from tiles | 2 | 16 | Close to saturated | 65536 | 0x00010000 | All VDFS catalogues | 2 | 17 | Photometric calibration probably subject to systematic error | 131072 | 0x00020000 | VVV only | 2 | 22 | Lies within a dither offset of the stacked frame boundary | 4194304 | 0x00400000 | All catalogues | 2 | 23 | Lies within the underexposed strip (or "ear") of a tile | 8388608 | 0x00800000 | All catalogues from tiles | 3 | 24 | Lies within an underexposed region of a tile due to missing detector | 16777216 | 0x01000000 | All catalogues from tiles | In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information. |
ppErrBits |
vvvListRemeasurement |
VVVv20100531 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBits |
vvvListRemeasurement |
VVVv20110718 |
additional WFAU post-processing error bits |
int |
4 |
|
0 |
meta.code |
ppErrBitsStatus |
MapFrameStatus |
SHARKSv20210222 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
SHARKSv20210421 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
ULTRAVISTADR4 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VHSv20201209 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VHSv20231101 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VHSv20240731 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VMCDEEPv20230713 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VMCDEEPv20240506 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VMCDR5 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VMCv20191212 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VMCv20210708 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VMCv20230816 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VMCv20240226 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VVVDR5 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
MapFrameStatus |
VVVXDR1 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme and this map product. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
SHARKSv20210222 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
SHARKSv20210421 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
ULTRAVISTADR4 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSDR1 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSDR2 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSDR3 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSDR4 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSDR5 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSDR6 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20120926 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20130417 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20140409 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20150108 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20160114 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20160507 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20170630 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20180419 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20201209 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20231101 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VHSv20240731 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIDEODR2 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIDEODR3 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIDEODR4 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIDEODR5 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIDEOv20100513 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIDEOv20111208 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGDR2 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGDR3 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGDR4 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20110714 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20111019 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20130417 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20140402 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20150421 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20151230 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20160406 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20161202 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VIKINGv20170715 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCDEEPv20230713 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCDEEPv20240506 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCDR1 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCDR2 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCDR3 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCDR4 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCDR5 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20110816 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20110909 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20120126 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20121128 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20130304 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20130805 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20140428 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20140903 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20150309 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20151218 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20160311 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20160822 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20170109 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20170411 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20171101 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20180702 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20181120 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20191212 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20210708 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20230816 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VMCv20240226 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VSAQC |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VVVDR1 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VVVDR2 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VVVDR5 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VVVXDR1 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VVVv20100531 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
ppErrBitsStatus |
ProgrammeFrame |
VVVv20110718 |
Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. |
int |
4 |
|
0 |
|
pPNe |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being an PNe. {catalogue TType keyword: PNe} |
float |
8 |
|
|
|
pPNeErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being an PNe. {catalogue TType keyword: PNe_err} |
float |
8 |
|
|
|
pPrfClass |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being the predicted class. {catalogue TType keyword: PRF_P_class} |
float |
8 |
|
|
|
pPrfExGal |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being extragalactic. {catalogue TType keyword: PRF_P_extragalactic} |
float |
8 |
|
|
|
previewv |
Multiframe |
SHARKSv20210222 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
SHARKSv20210421 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
ULTRAVISTADR4 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSDR1 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSDR2 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSDR3 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSDR4 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSDR5 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSDR6 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20120926 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20130417 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20140409 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20150108 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20160114 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20160507 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20170630 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20180419 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20201209 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20231101 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VHSv20240731 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIDEODR2 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIDEODR3 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIDEODR4 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIDEODR5 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIDEOv20100513 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIDEOv20111208 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGDR2 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGDR3 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGDR4 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20110714 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20111019 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20130417 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20140402 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20150421 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20151230 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20160406 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20161202 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VIKINGv20170715 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCDEEPv20230713 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCDEEPv20240506 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCDR1 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCDR2 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCDR3 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCDR4 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCDR5 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20110816 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20110909 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20120126 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20121128 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20130304 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20130805 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20140428 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20140903 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20150309 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20151218 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20160311 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20160822 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20170109 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20170411 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20171101 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20180702 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20181120 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20191212 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20210708 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20230816 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VMCv20240226 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VVVDR1 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VVVDR2 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VVVDR5 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VVVXDR1 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VVVv20100531 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
Multiframe |
VVVv20110718 |
Version of previ {image primary HDU keyword: PREVIEWV} |
varchar |
64 |
|
NONE |
|
previewv |
sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe |
VSAQC |
Version of previ |
varchar |
64 |
|
NONE |
|
prfClass |
vmcMLClassificationCatalogue |
VMCv20240226 |
Class predicted by the PRF. The class with the highest probability. {catalogue TType keyword: PRF_Class} |
varchar |
16 |
|
|
|
pRGB |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being an RGB star. {catalogue TType keyword: RGB} |
float |
8 |
|
|
|
pRGBErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being an RGB star. {catalogue TType keyword: RGB_err} |
float |
8 |
|
|
|
priam_flags |
gaia_source |
GAIADR2 |
Flags from Apsis-Priam analysis |
bigint |
8 |
|
|
meta.code |
priFlgLb |
rosat_bsc, rosat_fsc |
ROSAT |
priority flag L-broad |
tinyint |
1 |
|
|
meta.code |
priFlgLh |
rosat_bsc, rosat_fsc |
ROSAT |
priority flag L-hard |
tinyint |
1 |
|
|
meta.code |
priFlgLs |
rosat_bsc, rosat_fsc |
ROSAT |
priority flag L-soft |
tinyint |
1 |
|
|
meta.code |
priFlgMb |
rosat_bsc, rosat_fsc |
ROSAT |
priority flag M-broad |
tinyint |
1 |
|
|
meta.code |
priFlgMh |
rosat_bsc, rosat_fsc |
ROSAT |
priority flag M-hard |
tinyint |
1 |
|
|
meta.code |
priFlgMs |
rosat_bsc, rosat_fsc |
ROSAT |
priority flag M-soft |
tinyint |
1 |
|
|
meta.code |
primaryDetection |
StackObjectAttributes, StackObjectThin |
PS1DR2 |
Identifies if this row is the primary stack detection. |
tinyint |
1 |
|
255 |
|
PRIORITY |
agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss, supercos, twomass |
SIXDF |
survey weight |
smallint |
2 |
|
|
|
priOrSec |
sharksSource |
SHARKSv20210222 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
sharksSource |
SHARKSv20210421 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
ultravistaSource, ultravistaSourceRemeasurement |
ULTRAVISTADR4 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSDR1 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSDR2 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSDR3 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSDR4 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSDR5 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSDR6 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20120926 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20130417 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20140409 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20150108 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20160114 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20160507 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20170630 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20180419 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20201209 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20231101 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSource |
VHSv20240731 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vhsSourceRemeasurement |
VHSDR1 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
videoSource |
VIDEODR2 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
videoSource |
VIDEODR3 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
videoSource |
VIDEODR4 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
videoSource |
VIDEODR5 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
videoSource |
VIDEOv20100513 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
videoSource |
VIDEOv20111208 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
videoSourceRemeasurement |
VIDEOv20100513 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGDR2 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGDR3 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGDR4 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20110714 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20111019 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20130417 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20140402 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20150421 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20151230 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20160406 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20161202 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSource |
VIKINGv20170715 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSourceRemeasurement |
VIKINGv20110714 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingSourceRemeasurement |
VIKINGv20111019 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20160909 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20170124 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcPsfSource |
VMCv20180702 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcPsfSource |
VMCv20181120 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcPsfSource |
VMCv20191212 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcPsfSource |
VMCv20210708 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcPsfSource |
VMCv20230816 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcPsfSource |
VMCv20240226 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcPsfSource, vmcSource |
VMCDR5 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcSourceRemeasurement |
VMCv20110816 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcSourceRemeasurement |
VMCv20110909 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcdeepSource |
VMCDEEPv20230713 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vmcdeepSource |
VMCDEEPv20240506 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vvvPsfDaophotJKsSource, vvvPsfDophotZYJHKsSource, vvvSource |
VVVDR5 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vvvSourceRemeasurement |
VVVv20100531 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vvvSourceRemeasurement |
VVVv20110718 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) |
bigint |
8 |
|
|
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
priOrSec |
vvvxSource |
VVVXDR1 |
Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). |
bigint |
8 |
|
-99999999 |
meta.code |
Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID). |
PROB |
grs_ngpSource, grs_ranSource, grs_sgpSource |
TWODFGRS |
psi classification parameter: for eyeballed galaxies: psi*1000 = (10000 + abs(jon) + psi*100); for eyeballed non-galaxies: psi*1000 = -(10000 + abs(jon) + psi*100) |
real |
4 |
|
|
|
prob |
smashdr2_deep, smashdr2_object |
SMASH |
Average Source Extractor stellaricity probability value |
real |
4 |
|
|
|
prob |
smashdr2_source |
SMASH |
Source Extractor stellaricity probability value (0∼galaxy, 1∼star) |
real |
4 |
|
|
|
probGal |
wiseScosSvm |
WISExSCOSPZ |
Probability of being a galaxy {image primary HDU keyword: pgal} |
real |
4 |
|
-0.9999995e9 |
|
probQso |
wiseScosSvm |
WISExSCOSPZ |
Probability of being a QSO {image primary HDU keyword: pqso} |
real |
4 |
|
-0.9999995e9 |
|
probStar |
wiseScosSvm |
WISExSCOSPZ |
Probability of being a star {image primary HDU keyword: pstar} |
real |
4 |
|
-0.9999995e9 |
|
processingVersion |
Detection, StackObjectThin |
PS1DR2 |
Data release version. |
tinyint |
1 |
|
|
|
processingVersion |
ObjectThin |
PS1DR2 |
Data release version. |
tinyint |
1 |
|
|
meta.id;meta.software |
productID |
EpochFrameStatus |
SHARKSv20210421 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
ULTRAVISTADR4 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSDR5 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSDR6 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSv20160114 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSv20160507 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSv20170630 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSv20180419 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSv20201209 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSv20231101 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VHSv20240731 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VIKINGv20151230 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VIKINGv20160406 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VIKINGv20161202 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VIKINGv20170715 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCDEEPv20230713 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCDEEPv20240506 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCDR4 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCDR5 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20151218 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20160311 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20160822 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20170109 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20170411 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20171101 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20180702 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20181120 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20191212 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20210708 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20230816 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VMCv20240226 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VVVDR5 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus |
VVVXDR1 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
EpochFrameStatus, ProgrammeFrame |
SHARKSv20210222 |
Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} |
bigint |
8 |
|
-99999999 |
|
productID |
ExternalProduct |
SHARKSv20210421 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
ULTRAVISTADR4 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VHSv20180419 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VHSv20201209 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VHSv20231101 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VHSv20240731 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCDEEPv20230713 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCDEEPv20240506 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCDR5 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCv20180702 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCv20181120 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCv20191212 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCv20210708 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCv20230816 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VMCv20240226 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VVVDR5 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct |
VVVXDR1 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
ExternalProduct, ExternalProductCatalogue |
SHARKSv20210222 |
unique productID over all programmes |
int |
4 |
|
|
meta.id;meta.main |
productID |
RequiredDiffImage |
SHARKSv20210222 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
SHARKSv20210421 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
ULTRAVISTADR4 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSDR1 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSDR2 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSDR3 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSDR4 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSDR5 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSDR6 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20120926 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20130417 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20150108 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20160114 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20160507 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20170630 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20180419 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20201209 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20231101 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VHSv20240731 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIDEODR2 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIDEODR3 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIDEODR4 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIDEODR5 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIDEOv20100513 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIDEOv20111208 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGDR2 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGDR3 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGDR4 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20110714 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20111019 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20130417 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20150421 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20151230 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20160406 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20161202 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VIKINGv20170715 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCDEEPv20230713 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCDEEPv20240506 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCDR1 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCDR3 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCDR4 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCDR5 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20110816 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20110909 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20120126 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20121128 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20130304 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20130805 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20140428 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20140903 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20150309 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20151218 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20160311 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20160822 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20170109 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20170411 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20171101 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20180702 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20181120 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20191212 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20210708 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20230816 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VMCv20240226 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VVVDR1 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VVVDR2 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VVVDR5 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VVVXDR1 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VVVv20100531 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredDiffImage |
VVVv20110718 |
A unique identifier assigned to each required difference image product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
SHARKSv20210421 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
ULTRAVISTADR4 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSDR5 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSDR6 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSv20160114 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSv20160507 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSv20170630 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSv20180419 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSv20201209 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSv20231101 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VHSv20240731 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VIKINGv20151230 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VIKINGv20160406 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VIKINGv20161202 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VIKINGv20170715 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCDEEPv20230713 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCDEEPv20240506 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCDR4 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCDR5 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20151218 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20160311 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20160822 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20170109 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20170411 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20171101 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20180702 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20181120 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20191212 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20210708 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20230816 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VMCv20240226 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VVVDR5 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch |
VVVXDR1 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMergeLogMultiEpoch, RequiredStack, RequiredTile |
SHARKSv20210222 |
A unique identifier assigned to each required stack product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
SHARKSv20210222 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
SHARKSv20210421 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
ULTRAVISTADR4 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSDR1 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSDR2 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSDR3 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSDR4 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSDR5 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSDR6 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20120926 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20130417 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20150108 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20160114 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20160507 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20170630 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20180419 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20201209 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20231101 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VHSv20240731 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIDEODR2 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIDEODR3 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIDEODR4 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIDEODR5 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIDEOv20100513 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIDEOv20111208 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGDR2 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGDR3 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGDR4 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20110714 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20111019 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20130417 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20150421 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20151230 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20160406 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20161202 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VIKINGv20170715 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCDEEPv20230713 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCDEEPv20240506 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCDR1 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCDR3 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCDR4 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCDR5 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20110816 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20110909 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20120126 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20121128 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20130304 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20130805 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20140428 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20140903 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20150309 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20151218 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20160311 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20160822 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20170109 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20170411 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20171101 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20180702 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20181120 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20191212 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20210708 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20230816 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VMCv20240226 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VVVDR1 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VVVDR2 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VVVDR5 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VVVXDR1 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VVVv20100531 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productID |
RequiredMosaic |
VVVv20110718 |
A unique identifier assigned to each required mosaic product entry |
int |
4 |
|
|
?? |
productType |
EpochFrameStatus |
SHARKSv20210222 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
SHARKSv20210421 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
ULTRAVISTADR4 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSDR5 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSDR6 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSv20160114 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSv20160507 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSv20170630 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSv20180419 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSv20201209 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSv20231101 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VHSv20240731 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VIKINGv20151230 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VIKINGv20160406 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VIKINGv20161202 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VIKINGv20170715 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCDEEPv20230713 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCDEEPv20240506 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCDR4 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCDR5 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20151218 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20160311 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20160822 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20170109 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20170411 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20171101 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20180702 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20181120 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20191212 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20210708 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20230816 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VMCv20240226 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VVVDR5 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
EpochFrameStatus |
VVVXDR1 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
NONE |
|
productType |
ExternalProduct |
SHARKSv20210222 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
SHARKSv20210421 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
ULTRAVISTADR4 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSDR4 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSDR5 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSDR6 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20150108 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20160114 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20160507 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20170630 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20180419 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20201209 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20231101 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VHSv20240731 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIDEODR4 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIDEODR5 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIDEOv20111208 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIKINGDR4 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIKINGv20150421 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIKINGv20151230 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIKINGv20160406 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIKINGv20161202 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VIKINGv20170715 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCDEEPv20230713 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCDEEPv20240506 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCDR3 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCDR4 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCDR5 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20140428 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20140903 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20150309 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20151218 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20160311 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20160822 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20170109 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20170411 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20171101 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20180702 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20181120 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20191212 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20210708 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20230816 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VMCv20240226 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VVVDR5 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct |
VVVXDR1 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
ExternalProduct, ExternalProductCatalogue |
VHSDR3 |
The product type within the imported directory |
varchar |
16 |
|
|
?? |
productType |
RequiredListDrivenProduct |
VHSv20130417 |
product type of file to be extracted (stack, tile, mosaic) |
varchar |
8 |
|
NONE |
|
productType |
RequiredListDrivenProduct |
VIKINGv20130417 |
product type of file to be extracted (stack, tile, mosaic) |
varchar |
8 |
|
NONE |
|
productType |
RequiredListDrivenProduct |
VMCv20130805 |
product type of file to be extracted (stack, tile, mosaic) |
varchar |
8 |
|
NONE |
|
productType |
RequiredMergeLogMultiEpoch |
SHARKSv20210222 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
SHARKSv20210421 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
ULTRAVISTADR4 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSDR5 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSDR6 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSv20160114 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSv20160507 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSv20170630 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSv20180419 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSv20201209 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSv20231101 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VHSv20240731 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VIKINGv20151230 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VIKINGv20160406 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VIKINGv20161202 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VIKINGv20170715 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCDEEPv20230713 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCDEEPv20240506 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCDR4 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCDR5 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20151218 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20160311 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20160822 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20170109 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20170411 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20171101 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20180702 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20181120 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20191212 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20210708 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20230816 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VMCv20240226 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VVVDR5 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
productType |
RequiredMergeLogMultiEpoch |
VVVXDR1 |
Product type (stack,tile,mosaic) |
varchar |
16 |
|
|
|
PROGID |
agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss, supercos, twomass |
SIXDF |
programme ID |
smallint |
2 |
|
|
|
PROGID |
target |
SIXDF |
highest priority programme ID |
smallint |
2 |
|
|
|
PROGID_R |
spectra |
SIXDF |
programme ID in R frame |
varchar |
32 |
|
|
|
PROGID_V |
spectra |
SIXDF |
programme ID in V frame |
varchar |
32 |
|
|
|
programmeID |
CombinedFilters |
SHARKSv20210421 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
ULTRAVISTADR4 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VHSv20201209 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VHSv20231101 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VHSv20240731 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VMCDEEPv20230713 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VMCDEEPv20240506 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VMCDR5 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VMCv20191212 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VMCv20210708 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VMCv20230816 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VMCv20240226 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VVVDR5 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters |
VVVXDR1 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
CombinedFilters, ExternalProduct, ExternalProductCatalogue, MapFilterLupt, ProductLinks, ProgrammeCurationHistory, ProgrammeTable, RegionFieldLinks, RequiredDiffImage, RequiredFilters, RequiredMapAverages, RequiredMatchedApertureProduct, RequiredMergeLogMultiEpoch, RequiredMosaic, RequiredMosaicTopLevel, RequiredNeighbours, RequiredRegion, RequiredStack, RequiredTile, SExtractorInputParams |
SHARKSv20210222 |
the unique programme ID |
int |
4 |
|
|
meta.id |
programmeID |
EpochFrameStatus |
SHARKSv20210421 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
ULTRAVISTADR4 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSDR5 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSDR6 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSv20160114 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSv20160507 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSv20170630 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSv20180419 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSv20201209 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSv20231101 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VHSv20240731 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VIKINGv20151230 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VIKINGv20160406 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VIKINGv20161202 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VIKINGv20170715 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCDEEPv20230713 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCDEEPv20240506 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCDR4 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCDR5 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20151218 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20160311 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20160822 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20170109 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20170411 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20171101 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20180702 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20181120 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20191212 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20210708 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20230816 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VMCv20240226 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VVVDR5 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus |
VVVXDR1 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
EpochFrameStatus, ProgrammeFrame |
SHARKSv20210222 |
VSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
SHARKSv20210222 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
SHARKSv20210421 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
ULTRAVISTADR4 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VHSv20201209 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VHSv20231101 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VHSv20240731 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VMCDEEPv20230713 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VMCDEEPv20240506 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VMCDR5 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VMCv20191212 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VMCv20210708 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VMCv20230816 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VMCv20240226 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VVVDR5 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
MapFrameStatus |
VVVXDR1 |
WSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
SHARKSv20210421 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VHSv20201209 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VHSv20231101 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VHSv20240731 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VMCDEEPv20230713 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VMCDEEPv20240506 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VMCv20210708 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VMCv20230816 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VMCv20240226 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VVVDR5 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams |
VVVXDR1 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
PreviousCalibParams, ProblemFrames |
SHARKSv20210222 |
VSA assigned programme UID |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
Programme |
SHARKSv20210222 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
SHARKSv20210421 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
ULTRAVISTADR4 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSDR1 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSDR2 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSDR3 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSDR4 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSDR5 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSDR6 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20120926 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20130417 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20150108 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20160114 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20160507 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20170630 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20180419 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20201209 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20231101 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VHSv20240731 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIDEODR2 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIDEODR3 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIDEODR4 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIDEODR5 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIDEOv20100513 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIDEOv20111208 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGDR2 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGDR3 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGDR4 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20110714 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20111019 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20130417 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20150421 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20151230 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20160406 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20161202 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VIKINGv20170715 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCDEEPv20230713 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCDEEPv20240506 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCDR1 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCDR3 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCDR4 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCDR5 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20110816 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20110909 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20120126 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20121128 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20130304 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20130805 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20140428 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20140903 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20150309 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20151218 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20160311 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20160822 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20170109 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20170411 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20171101 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20180702 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20181120 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20191212 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20210708 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20230816 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VMCv20240226 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VSAQC |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VVVDR1 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VVVDR2 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VVVDR5 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VVVXDR1 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VVVv20100531 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
Programme |
VVVv20110718 |
UID of the archived programme coded as above |
int |
4 |
|
|
meta.id |
programmeID |
SurveyProgrammes |
SHARKSv20210222 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
SHARKSv20210421 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
ULTRAVISTADR4 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSDR1 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSDR2 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSDR3 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSDR4 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSDR5 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSDR6 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20120926 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20130417 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20150108 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20160114 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20160507 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20170630 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20180419 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20201209 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20231101 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VHSv20240731 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIDEODR2 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIDEODR3 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIDEODR4 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIDEODR5 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIDEOv20100513 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIDEOv20111208 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGDR2 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGDR3 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGDR4 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20110714 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20111019 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20130417 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20150421 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20151230 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20160406 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20161202 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VIKINGv20170715 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCDEEPv20230713 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCDEEPv20240506 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCDR1 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCDR3 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCDR4 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCDR5 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20110816 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20110909 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20120126 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20121128 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20130304 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20130805 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20140428 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20140903 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20150309 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20151218 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20160311 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20160822 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20170109 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20170411 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20171101 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20180702 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20181120 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20191212 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20210708 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20230816 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VMCv20240226 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VSAQC |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VVVDR1 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VVVDR2 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VVVDR5 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VVVXDR1 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VVVv20100531 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
programmeID |
SurveyProgrammes |
VVVv20110718 |
VSA assigned programme UID {image primary HDU keyword: PROJECT} |
int |
4 |
|
-99999999 |
meta.id |
project |
Multiframe |
SHARKSv20210222 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
SHARKSv20210421 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
ULTRAVISTADR4 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSDR1 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSDR2 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSDR3 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSDR4 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSDR5 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSDR6 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20120926 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20130417 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20140409 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20150108 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20160114 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20160507 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20170630 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20180419 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20201209 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20231101 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VHSv20240731 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIDEODR2 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIDEODR3 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIDEODR4 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIDEODR5 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIDEOv20100513 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIDEOv20111208 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGDR2 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGDR3 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGDR4 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20110714 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20111019 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20130417 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20140402 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20150421 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20151230 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20160406 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20161202 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VIKINGv20170715 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCDEEPv20230713 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCDEEPv20240506 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCDR1 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCDR2 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCDR3 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCDR4 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCDR5 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20110816 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20110909 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20120126 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20121128 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20130304 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20130805 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20140428 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20140903 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20150309 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20151218 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20160311 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20160822 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20170109 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20170411 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20171101 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20180702 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20181120 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20191212 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20210708 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20230816 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VMCv20240226 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VVVDR1 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VVVDR2 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VVVDR5 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VVVXDR1 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VVVv20100531 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
Multiframe |
VVVv20110718 |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
project |
sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe |
VSAQC |
Time-allocation code |
varchar |
64 |
|
NONE |
meta.bib |
projectionID |
ObjectThin |
PS1DR2 |
Projection cell identifier. |
smallint |
2 |
|
-1 |
meta.id |
projectionID |
StackObjectThin |
PS1DR2 |
Projection cell identifier. |
smallint |
2 |
|
-1 |
|
projp1 |
CurrentAstrometry |
SHARKSv20210222 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
SHARKSv20210421 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
ULTRAVISTADR4 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSDR1 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSDR2 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSDR3 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSDR4 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSDR5 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSDR6 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20120926 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20130417 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20140409 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20150108 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20160114 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20160507 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20170630 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20180419 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20201209 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20231101 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VHSv20240731 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIDEODR2 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIDEODR3 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIDEODR4 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIDEODR5 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIDEOv20100513 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIDEOv20111208 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGDR2 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGDR3 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGDR4 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20110714 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20111019 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20130417 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20140402 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20150421 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20151230 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20160406 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20161202 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VIKINGv20170715 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCDEEPv20230713 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCDEEPv20240506 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCDR1 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCDR2 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCDR3 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCDR4 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCDR5 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20110816 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20110909 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20120126 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20121128 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20130304 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20130805 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20140428 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20140903 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20150309 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20151218 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20160311 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20160822 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20170109 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20170411 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20171101 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20180702 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20181120 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20191212 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20210708 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20230816 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VMCv20240226 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VVVDR1 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VVVDR2 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VVVDR5 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VVVXDR1 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VVVv20100531 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
CurrentAstrometry |
VVVv20110718 |
Old style WCS {image extension keyword: PROJP1} |
float |
8 |
|
-9.999995e+08 |
|
projp1 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Old style WCS |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
SHARKSv20210222 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
SHARKSv20210421 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
ULTRAVISTADR4 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSDR1 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSDR2 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSDR3 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSDR4 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSDR5 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSDR6 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20120926 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20130417 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20140409 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20150108 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20160114 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20160507 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20170630 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20180419 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20201209 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20231101 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VHSv20240731 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIDEODR2 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIDEODR3 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIDEODR4 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIDEODR5 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIDEOv20100513 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIDEOv20111208 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGDR2 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGDR3 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGDR4 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20110714 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20111019 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20130417 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20140402 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20150421 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20151230 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20160406 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20161202 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VIKINGv20170715 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCDEEPv20230713 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCDEEPv20240506 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCDR1 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCDR2 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCDR3 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCDR4 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCDR5 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20110816 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20110909 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20120126 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20121128 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20130304 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20130805 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20140428 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20140903 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20150309 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20151218 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20160311 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20160822 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20170109 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20170411 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20171101 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20180702 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20181120 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20191212 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20210708 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20230816 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VMCv20240226 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VVVDR1 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VVVDR2 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VVVDR5 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VVVXDR1 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VVVv20100531 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
CurrentAstrometry |
VVVv20110718 |
Old style WCS {image extension keyword: PROJP3} |
float |
8 |
|
-9.999995e+08 |
|
projp3 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Old style WCS |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
SHARKSv20210222 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
SHARKSv20210421 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
ULTRAVISTADR4 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSDR1 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSDR2 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSDR3 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSDR4 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSDR5 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSDR6 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20120926 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20130417 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20140409 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20150108 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20160114 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20160507 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20170630 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20180419 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20201209 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20231101 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VHSv20240731 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIDEODR2 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIDEODR3 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIDEODR4 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIDEODR5 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIDEOv20100513 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIDEOv20111208 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGDR2 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGDR3 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGDR4 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20110714 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20111019 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20130417 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20140402 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20150421 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20151230 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20160406 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20161202 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VIKINGv20170715 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCDEEPv20230713 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCDEEPv20240506 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCDR1 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCDR2 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCDR3 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCDR4 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCDR5 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20110816 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20110909 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20120126 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20121128 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20130304 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20130805 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20140428 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20140903 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20150309 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20151218 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20160311 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20160822 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20170109 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20170411 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20171101 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20180702 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20181120 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20191212 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20210708 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20230816 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VMCv20240226 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VVVDR1 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VVVDR2 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VVVDR5 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VVVXDR1 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VVVv20100531 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
CurrentAstrometry |
VVVv20110718 |
Old style WCS {image extension keyword: PROJP5} |
float |
8 |
|
-9.999995e+08 |
|
projp5 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Old style WCS |
float |
8 |
|
-9.999995e+08 |
|
propPeriod |
Programme |
SHARKSv20210222 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
SHARKSv20210421 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
ULTRAVISTADR4 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSDR1 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSDR2 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSDR3 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSDR4 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSDR5 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSDR6 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20120926 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20130417 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20150108 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20160114 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20160507 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20170630 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20180419 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20201209 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20231101 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VHSv20240731 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIDEODR2 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIDEODR3 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIDEODR4 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIDEODR5 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIDEOv20100513 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIDEOv20111208 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGDR2 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGDR3 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGDR4 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20110714 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20111019 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20130417 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20150421 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20151230 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20160406 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20161202 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VIKINGv20170715 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCDEEPv20230713 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCDEEPv20240506 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCDR1 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCDR3 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCDR4 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCDR5 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20110816 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20110909 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20120126 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20121128 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20130304 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20130805 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20140428 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20140903 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20150309 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20151218 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20160311 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20160822 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20170109 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20170411 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20171101 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20180702 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20181120 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20191212 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20210708 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20230816 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VMCv20240226 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VSAQC |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VVVDR1 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VVVDR2 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VVVDR5 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VVVXDR1 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VVVv20100531 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
propPeriod |
Programme |
VVVv20110718 |
the proprietory period for any data taken for this programme in months, e.g. 12 for open time. |
int |
4 |
months |
|
time.period |
proprietary |
Survey |
SHARKSv20210222 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
SHARKSv20210421 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
ULTRAVISTADR4 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSDR1 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSDR2 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSDR3 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSDR4 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSDR5 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSDR6 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20120926 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20130417 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20150108 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20160114 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20160507 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20170630 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20180419 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20201209 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20231101 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VHSv20240731 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIDEODR2 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIDEODR3 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIDEODR4 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIDEODR5 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIDEOv20100513 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIDEOv20111208 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGDR2 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGDR3 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGDR4 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20110714 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20111019 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20130417 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20150421 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20151230 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20160406 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20161202 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VIKINGv20170715 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCDEEPv20230713 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCDEEPv20240506 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCDR1 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCDR3 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCDR4 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCDR5 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20110816 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20110909 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20120126 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20121128 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20130304 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20130805 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20140428 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20140903 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20150309 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20151218 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20160311 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20160822 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20170109 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20170411 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20171101 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20180702 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20181120 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20191212 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20210708 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20230816 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VMCv20240226 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VSAQC |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VVVDR1 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VVVDR2 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VVVDR5 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VVVXDR1 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VVVv20100531 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
proprietary |
Survey |
VVVv20110718 |
Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) |
tinyint |
1 |
|
|
?? |
prox |
masterDR2 |
SKYMAPPER |
Distance to next-closest DR2 source (=15 if no sources within 15 arcsec) |
real |
4 |
arcsec |
|
pos.angDistance |
prox |
twomass_psc, twomass_xsc |
TWOMASS |
Proximity. |
real |
4 |
arcsec |
|
pos.angDistance |
prox |
tycho2 |
GAIADR1 |
Proximity indicator |
smallint |
2 |
0.1 arcsec |
|
pos.angDistance |
prox_id |
masterDR2 |
SKYMAPPER |
object_id of next-closest DR2 source |
bigint |
8 |
|
|
meta.id |
pRSG |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being an RSG star. {catalogue TType keyword: RSG} |
float |
8 |
|
|
|
pRSGErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being an RSG star. {catalogue TType keyword: RSG_err} |
float |
8 |
|
|
|
ps1_dr1_dist |
masterDR2 |
SKYMAPPER |
Distance on sky to closest Pan-STARRS1 DR1 source |
real |
4 |
arcsec |
|
pos.angDistance |
ps1_dr1_id |
masterDR2 |
SKYMAPPER |
Unique identifier (objID) of closest Pan-STARRS1 DR1 source |
bigint |
8 |
|
|
meta.id.cross |
pSaturated |
sharksSource |
SHARKSv20210222 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
sharksSource |
SHARKSv20210421 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
ultravistaSource, ultravistaSourceRemeasurement |
ULTRAVISTADR4 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSDR1 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSDR2 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSDR3 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSDR4 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSDR5 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSDR6 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20120926 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20130417 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20140409 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20150108 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20160114 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20160507 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20170630 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20180419 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20201209 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20231101 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vhsSource |
VHSv20240731 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
videoSource |
VIDEODR2 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
videoSource |
VIDEODR3 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
videoSource |
VIDEODR4 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
videoSource |
VIDEODR5 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
videoSource |
VIDEOv20100513 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
videoSource |
VIDEOv20111208 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGDR2 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGDR3 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGDR4 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20110714 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20111019 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20130417 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20140402 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20150421 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20151230 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20160406 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20161202 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingSource |
VIKINGv20170715 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20160909 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20170124 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCDR2 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCDR3 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCDR4 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCDR5 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20110816 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20110909 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20120126 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20121128 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20130304 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20130805 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20140428 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20140903 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20150309 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20151218 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20160311 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20160822 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20170109 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20170411 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20171101 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20180702 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20181120 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20191212 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20210708 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20230816 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource |
VMCv20240226 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcSource, vmcSynopticSource |
VMCDR1 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcdeepSource |
VMCDEEPv20240506 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vmcdeepSource, vmcdeepSynopticSource |
VMCDEEPv20230713 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vvvSource |
VVVDR2 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vvvSource |
VVVDR5 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vvvSource |
VVVv20100531 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vvvSource |
VVVv20110718 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vvvSource, vvvSynopticSource |
VVVDR1 |
Probability that the source is saturated |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pSaturated |
vvvxSource |
VVVXDR1 |
Probability that the source is saturated |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pseudocolour |
gaia_source |
GAIAEDR3 |
Astrometrically determined pseudocolour of the source |
real |
4 |
micron^-1 |
|
em.wavenumber |
pseudocolour_error |
gaia_source |
GAIAEDR3 |
STandard error of the pseudocolour of the source |
real |
4 |
micron^-1 |
|
em.wavenumber |
psfChiSq |
Detection |
PS1DR2 |
Reduced chi squared value of the PSF model fit. |
real |
4 |
|
-999 |
|
psfCore |
Detection |
PS1DR2 |
PSF core parameter k, where F = F0 / (1 + k r^2 + r^3.33). |
real |
4 |
|
-999 |
|
psfFitChi2 |
sharksDetection |
SHARKSv20210222 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
sharksDetection |
SHARKSv20210421 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSDR2 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSDR3 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSDR4 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSDR5 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSDR6 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20120926 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20130417 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20140409 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20150108 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20160114 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20160507 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20170630 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20180419 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20201209 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20231101 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection |
VHSv20240731 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
|
psfFitChi2 |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
|
psfFitChi2 |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
|
psfFitChi2 |
videoListRemeasurement |
VIDEOv20100513 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGDR2 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGDR3 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGDR4 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20111019 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20130417 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20140402 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20150421 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20151230 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20160406 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20161202 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection |
VIKINGv20170715 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCDR1 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCDR2 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCDR3 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCDR4 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCDR5 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20110909 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20120126 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20121128 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20130304 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20130805 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20140428 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20140903 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20150309 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20151218 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20160311 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20160822 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20170109 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20170411 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20171101 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20180702 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20181120 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20191212 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20210708 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20230816 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection |
VMCv20240226 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcdeepDetection |
VMCDEEPv20230713 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vmcdeepDetection |
VMCDEEPv20240506 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vvvDetection |
VVVDR1 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vvvDetection |
VVVDR2 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitChi2 |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} |
real |
4 |
|
-0.9999995e9 |
stat.stdev |
psfFitDof |
sharksDetection |
SHARKSv20210222 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
sharksDetection |
SHARKSv20210421 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSDR2 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSDR3 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSDR4 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSDR5 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSDR6 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20120926 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20130417 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20140409 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20150108 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20160114 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20160507 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20170630 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20180419 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20201209 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20231101 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection |
VHSv20240731 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
|
psfFitDof |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
|
psfFitDof |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
|
psfFitDof |
videoListRemeasurement |
VIDEOv20100513 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGDR2 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGDR3 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGDR4 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20111019 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20130417 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20140402 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20150421 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20151230 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20160406 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20161202 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection |
VIKINGv20170715 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCDR1 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCDR2 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCDR3 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCDR4 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCDR5 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20110909 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20120126 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20121128 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20130304 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20130805 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20140428 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20140903 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20150309 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20151218 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20160311 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20160822 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20170109 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20170411 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20171101 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20180702 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20181120 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20191212 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20210708 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20230816 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection |
VMCv20240226 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcdeepDetection |
VMCDEEPv20230713 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vmcdeepDetection |
VMCDEEPv20240506 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vvvDetection |
VVVDR1 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vvvDetection |
VVVDR2 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitDof |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} |
smallint |
2 |
|
-9999 |
stat.fit.dof |
psfFitX |
sharksDetection |
SHARKSv20210222 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
sharksDetection |
SHARKSv20210421 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_X} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSDR2 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSDR3 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSDR4 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSDR5 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSDR6 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20120926 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20130417 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20140409 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20150108 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20160114 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20160507 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20170630 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20180419 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20201209 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20231101 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection |
VHSv20240731 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_fit_X} |
real |
4 |
|
-0.9999995e9 |
|
psfFitX |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_fit_X} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_X} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_fit_X} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_fit_X} |
real |
4 |
|
-0.9999995e9 |
|
psfFitX |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_fit_X} |
real |
4 |
|
-0.9999995e9 |
|
psfFitX |
videoListRemeasurement |
VIDEOv20100513 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGDR2 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGDR3 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGDR4 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20111019 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20130417 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20140402 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20150421 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20151230 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20160406 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20161202 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection |
VIKINGv20170715 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCDR1 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCDR2 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCDR3 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCDR4 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCDR5 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20110909 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20120126 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20121128 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20130304 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20130805 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20140428 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20140903 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20150309 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20151218 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20160311 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20160822 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20170109 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20170411 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20171101 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20180702 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20181120 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20191212 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20210708 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20230816 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection |
VMCv20240226 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcdeepDetection |
VMCDEEPv20230713 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vmcdeepDetection |
VMCDEEPv20240506 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vvvDetection |
VVVDR1 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vvvDetection |
VVVDR2 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitX |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.x;instr.plate |
psfFitXerr |
sharksDetection |
SHARKSv20210222 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
sharksDetection |
SHARKSv20210421 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSDR2 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSDR3 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSDR4 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSDR5 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSDR6 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20120926 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20130417 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20140409 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20150108 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20160114 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20160507 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20170630 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20180419 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20201209 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20231101 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection |
VHSv20240731 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFitXerr |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitXerr |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitXerr |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitXerr |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFitXerr |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFitXerr |
videoListRemeasurement |
VIDEOv20100513 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGDR2 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGDR3 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGDR4 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20111019 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20130417 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20140402 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20150421 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20151230 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20160406 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20161202 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection |
VIKINGv20170715 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCDR1 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCDR2 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCDR3 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCDR4 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCDR5 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20110909 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20120126 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20121128 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20130304 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20130805 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20140428 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20140903 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20150309 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20151218 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20160311 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20160822 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20170109 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20170411 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20171101 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20180702 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20181120 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20191212 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20210708 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20230816 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection |
VMCv20240226 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcdeepDetection |
VMCDEEPv20230713 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vmcdeepDetection |
VMCDEEPv20240506 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vvvDetection |
VVVDR1 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vvvDetection |
VVVDR2 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitXerr |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitY |
sharksDetection |
SHARKSv20210222 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
sharksDetection |
SHARKSv20210421 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSDR2 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSDR3 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSDR4 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSDR5 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSDR6 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20120926 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20130417 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20140409 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20150108 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20160114 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20160507 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20170630 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20180419 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20201209 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20231101 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection |
VHSv20240731 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
|
-0.9999995e9 |
|
psfFitY |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
|
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
|
-0.9999995e9 |
|
psfFitY |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
|
-0.9999995e9 |
|
psfFitY |
videoListRemeasurement |
VIDEOv20100513 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGDR2 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGDR3 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGDR4 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20111019 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20130417 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20140402 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20150421 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20151230 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20160406 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20161202 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection |
VIKINGv20170715 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCDR1 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCDR2 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCDR3 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCDR4 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCDR5 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20110909 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20120126 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20121128 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20130304 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20130805 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20140428 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20140903 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20150309 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20151218 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20160311 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20160822 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20170109 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20170411 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20171101 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20180702 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20181120 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20191212 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20210708 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20230816 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection |
VMCv20240226 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcdeepDetection |
VMCDEEPv20230713 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vmcdeepDetection |
VMCDEEPv20240506 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vvvDetection |
VVVDR1 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vvvDetection |
VVVDR2 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitY |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} |
real |
4 |
pixels |
-0.9999995e9 |
pos.cartesian.y;instr.plate |
psfFitYerr |
sharksDetection |
SHARKSv20210222 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
sharksDetection |
SHARKSv20210421 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSDR2 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSDR3 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSDR4 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSDR5 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSDR6 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20120926 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20130417 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20140409 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20150108 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20160114 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20160507 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20170630 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20180419 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20201209 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20231101 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection |
VHSv20240731 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFitYerr |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitYerr |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitYerr |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFitYerr |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFitYerr |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFitYerr |
videoListRemeasurement |
VIDEOv20100513 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGDR2 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGDR3 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGDR4 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20111019 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20130417 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20140402 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20150421 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20151230 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20160406 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20161202 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection |
VIKINGv20170715 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCDR1 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCDR2 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCDR3 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCDR4 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCDR5 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20110909 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20120126 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20121128 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20130304 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20130805 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20140428 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20140903 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20150309 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20151218 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20160311 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20160822 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20170109 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20170411 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20171101 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20180702 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20181120 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20191212 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20210708 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20230816 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection |
VMCv20240226 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcdeepDetection |
VMCDEEPv20230713 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vmcdeepDetection |
VMCDEEPv20240506 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vvvDetection |
VVVDR1 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vvvDetection |
VVVDR2 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFitYerr |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} |
real |
4 |
pixels |
-0.9999995e9 |
stat.error |
psfFlux |
Detection |
PS1DR2 |
Flux from PSF fit. |
real |
4 |
Janskys |
-999 |
|
psfFlux |
sharksDetection |
SHARKSv20210222 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
sharksDetection |
SHARKSv20210421 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_flux} |
real |
4 |
|
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSDR2 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vhsDetection |
VHSDR3 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSDR4 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSDR5 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSDR6 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20120926 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20130417 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20140409 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20150108 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20160114 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20160507 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20170630 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20180419 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20201209 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20231101 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection |
VHSv20240731 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_flux} |
real |
4 |
|
-0.9999995e9 |
|
psfFlux |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_flux} |
real |
4 |
|
-0.9999995e9 |
phot.count |
psfFlux |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_flux} |
real |
4 |
|
-0.9999995e9 |
phot.count |
psfFlux |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_flux} |
real |
4 |
|
-0.9999995e9 |
phot.count |
psfFlux |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_flux} |
real |
4 |
|
-0.9999995e9 |
|
psfFlux |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_flux} |
real |
4 |
|
-0.9999995e9 |
|
psfFlux |
videoListRemeasurement |
VIDEOv20100513 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vikingDetection |
VIKINGDR2 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vikingDetection |
VIKINGDR3 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGDR4 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGv20111019 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vikingDetection |
VIKINGv20130417 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGv20140402 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGv20150421 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGv20151230 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGv20160406 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGv20161202 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection |
VIKINGv20170715 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vmcDetection |
VMCDR1 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vmcDetection |
VMCDR2 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCDR3 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCDR4 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCDR5 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20110909 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vmcDetection |
VMCv20120126 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vmcDetection |
VMCv20121128 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20130304 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20130805 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20140428 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20140903 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20150309 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20151218 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20160311 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20160822 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20170109 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20170411 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20171101 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20180702 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20181120 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20191212 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20210708 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20230816 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection |
VMCv20240226 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFlux |
vmcdeepDetection |
VMCDEEPv20230713 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vmcdeepDetection |
VMCDEEPv20240506 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vvvDetection |
VVVDR1 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vvvDetection |
VVVDR2 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count |
psfFlux |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
PSF-fitted flux {catalogue TType keyword: PSF_flux} |
real |
4 |
ADU |
-0.9999995e9 |
phot.count;em.opt |
psfFluxErr |
Detection |
PS1DR2 |
Error on flux from PSF fit. |
real |
4 |
Janskys |
-999 |
|
psfFluxErr |
sharksDetection |
SHARKSv20210222 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
sharksDetection |
SHARKSv20210421 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output {catalogue TType keyword: PSF_flux_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSDR2 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSDR3 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSDR4 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSDR5 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSDR6 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20120926 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20130417 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20140409 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20150108 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20160114 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20160507 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20170630 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20180419 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20201209 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20231101 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection |
VHSv20240731 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
videoDetection |
VIDEODR2 |
Not available in SE output {catalogue TType keyword: PSF_flux_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFluxErr |
videoDetection |
VIDEODR3 |
Not available in SE output {catalogue TType keyword: PSF_flux_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFluxErr |
videoDetection |
VIDEODR4 |
Not available in SE output {catalogue TType keyword: PSF_flux_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFluxErr |
videoDetection |
VIDEODR5 |
Not available in SE output {catalogue TType keyword: PSF_flux_err} |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfFluxErr |
videoDetection |
VIDEOv20100513 |
Not available in SE output {catalogue TType keyword: PSF_flux_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFluxErr |
videoDetection |
VIDEOv20111208 |
Not available in SE output {catalogue TType keyword: PSF_flux_err} |
real |
4 |
|
-0.9999995e9 |
|
psfFluxErr |
videoListRemeasurement |
VIDEOv20100513 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGDR2 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGDR3 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGDR4 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20111019 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20130417 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20140402 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20150421 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20151230 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20160406 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20161202 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection |
VIKINGv20170715 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCDR1 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCDR2 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCDR3 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCDR4 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCDR5 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20110909 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20120126 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20121128 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20130304 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20130805 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20140428 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20140903 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20150309 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20151218 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20160311 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20160822 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20170109 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20170411 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20171101 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20180702 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20181120 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20191212 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20210708 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20230816 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection |
VMCv20240226 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcdeepDetection |
VMCDEEPv20230713 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vmcdeepDetection |
VMCDEEPv20240506 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vvvDetection |
VVVDR1 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vvvDetection |
VVVDR2 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfFluxErr |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} |
real |
4 |
ADU |
-0.9999995e9 |
stat.error |
psfID |
vmcPsfCatalogue |
VMCDR3 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCDR4 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20121128 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20140428 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20140903 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20150309 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20151218 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20160311 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20160822 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20170109 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20170411 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfCatalogue |
VMCv20171101 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfDetections |
VMCv20180702 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vmcPsfDetections |
VMCv20181120 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfID |
vvvPsfDaophotJKsSource, vvvPsfDophotZYJHKsSource |
VVVDR5 |
UID (unique over entire VSA via programme ID prefix) of this merged detection as assigned by merge algorithm |
bigint |
8 |
|
|
meta.id;meta.main |
psfLikelihood |
Detection |
PS1DR2 |
Likelihood that this detection is best fit by a PSF. |
real |
4 |
|
-999 |
|
psfMag |
sharksDetection |
SHARKSv20210222 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
sharksDetection |
SHARKSv20210421 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSDR2 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vhsDetection |
VHSDR3 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vhsDetection |
VHSDR4 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSDR5 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSDR6 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20120926 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vhsDetection |
VHSv20130417 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vhsDetection |
VHSv20140409 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vhsDetection |
VHSv20150108 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20160114 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20160507 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20170630 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20180419 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20201209 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20231101 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection |
VHSv20240731 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
videoDetection |
VIDEODR2 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
|
psfMag |
videoDetection |
VIDEODR3 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.fit.param |
psfMag |
videoDetection |
VIDEODR4 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
videoDetection |
VIDEODR5 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
videoDetection |
VIDEOv20100513 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
|
psfMag |
videoDetection |
VIDEOv20111208 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
|
psfMag |
videoListRemeasurement |
VIDEOv20100513 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vikingDetection |
VIKINGDR2 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vikingDetection |
VIKINGDR3 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vikingDetection |
VIKINGDR4 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vikingDetection |
VIKINGv20111019 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vikingDetection |
VIKINGv20130417 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vikingDetection |
VIKINGv20140402 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vikingDetection |
VIKINGv20150421 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vikingDetection |
VIKINGv20151230 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vikingDetection |
VIKINGv20160406 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vikingDetection |
VIKINGv20161202 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vikingDetection |
VIKINGv20170715 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vmcDetection |
VMCDR1 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vmcDetection |
VMCDR2 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vmcDetection |
VMCDR3 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCDR4 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCDR5 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20110909 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vmcDetection |
VMCv20120126 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vmcDetection |
VMCv20121128 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vmcDetection |
VMCv20130304 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vmcDetection |
VMCv20130805 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vmcDetection |
VMCv20140428 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vmcDetection |
VMCv20140903 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20150309 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20151218 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20160311 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20160822 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20170109 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20170411 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20171101 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20180702 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20181120 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20191212 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20210708 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20230816 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection |
VMCv20240226 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMag |
vmcdeepDetection |
VMCDEEPv20230713 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vmcdeepDetection |
VMCDEEPv20240506 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vvvDetection |
VVVDR1 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vvvDetection |
VVVDR2 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param |
psfMag |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.fit.param;phot.mag |
psfMag |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
PHOT_PROFILE |
psfMagErr |
sharksDetection |
SHARKSv20210222 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
sharksDetection |
SHARKSv20210421 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
ultravistaDetection |
ULTRAVISTADR4 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSDR2 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vhsDetection |
VHSDR3 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vhsDetection |
VHSDR4 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSDR5 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSDR6 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20120926 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vhsDetection |
VHSv20130417 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vhsDetection |
VHSv20140409 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vhsDetection |
VHSv20150108 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20160114 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20160507 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20170630 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20180419 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20201209 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20231101 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection |
VHSv20240731 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vhsDetection, vhsListRemeasurement |
VHSDR1 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
videoDetection |
VIDEODR2 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
|
psfMagErr |
videoDetection |
VIDEODR3 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.error |
psfMagErr |
videoDetection |
VIDEODR4 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
videoDetection |
VIDEODR5 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
videoDetection |
VIDEOv20100513 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
|
psfMagErr |
videoDetection |
VIDEOv20111208 |
Not available in SE output |
real |
4 |
|
-0.9999995e9 |
|
psfMagErr |
videoListRemeasurement |
VIDEOv20100513 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vikingDetection |
VIKINGDR2 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vikingDetection |
VIKINGDR3 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vikingDetection |
VIKINGDR4 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vikingDetection |
VIKINGv20111019 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vikingDetection |
VIKINGv20130417 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vikingDetection |
VIKINGv20140402 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vikingDetection |
VIKINGv20150421 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vikingDetection |
VIKINGv20151230 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vikingDetection |
VIKINGv20160406 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vikingDetection |
VIKINGv20161202 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vikingDetection |
VIKINGv20170715 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vikingDetection, vikingListRemeasurement |
VIKINGv20110714 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCDR1 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCDR2 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCDR3 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCDR4 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCDR5 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20110909 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCv20120126 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCv20121128 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCv20130304 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCv20130805 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCv20140428 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcDetection |
VMCv20140903 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20150309 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20151218 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20160311 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20160822 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20170109 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20170411 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20171101 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20180702 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20181120 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20191212 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20210708 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20230816 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection |
VMCv20240226 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcDetection, vmcListRemeasurement |
VMCv20110816 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vmcdeepDetection |
VMCDEEPv20230713 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vmcdeepDetection |
VMCDEEPv20240506 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vvvDetection |
VVVDR1 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vvvDetection |
VVVDR2 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMagErr |
vvvDetection, vvvDetectionPawPrints, vvvDetectionTiles |
VVVDR5 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error;phot.mag |
psfMagErr |
vvvDetection, vvvListRemeasurement |
VVVv20100531 |
Error on PSF-fitted calibrated magnitude |
real |
4 |
mag |
-0.9999995e9 |
stat.error |
psfMajorFWHM |
Detection |
PS1DR2 |
PSF major axis FWHM. |
real |
4 |
arcsec |
-999 |
|
psfMinorFWHM |
Detection |
PS1DR2 |
PSF minor axis FWHM. |
real |
4 |
arcsec |
-999 |
|
psfQf |
Detection |
PS1DR2 |
PSF coverage factor. |
real |
4 |
|
-999 |
|
psfQfPerfect |
Detection |
PS1DR2 |
PSF weighted fraction of pixels totally unmasked. |
real |
4 |
|
-999 |
|
psfSourceID |
vmcPsfSource |
VMCDR5 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfSourceID |
vmcPsfSource |
VMCv20180702 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfSourceID |
vmcPsfSource |
VMCv20181120 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfSourceID |
vmcPsfSource |
VMCv20191212 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfSourceID |
vmcPsfSource |
VMCv20210708 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfSourceID |
vmcPsfSource |
VMCv20230816 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfSourceID |
vmcPsfSource |
VMCv20240226 |
UID of VMC PSF extracted objects |
bigint |
8 |
|
|
meta.id;meta.main |
psfTheta |
Detection |
PS1DR2 |
PSF major axis orientation. |
real |
4 |
degrees |
-999 |
|
pStar |
sharksSource |
SHARKSv20210222 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
sharksSource |
SHARKSv20210421 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
ultravistaSource, ultravistaSourceRemeasurement |
ULTRAVISTADR4 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSDR1 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSDR2 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSDR3 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSDR4 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSDR5 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSDR6 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20120926 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20130417 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20140409 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20150108 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20160114 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20160507 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20170630 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20180419 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20201209 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20231101 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vhsSource |
VHSv20240731 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
videoSource |
VIDEODR2 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
videoSource |
VIDEODR3 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
videoSource |
VIDEODR4 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
videoSource |
VIDEODR5 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
videoSource |
VIDEOv20100513 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
videoSource |
VIDEOv20111208 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGDR2 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGDR3 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGDR4 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20110714 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20111019 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20130417 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20140402 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20150421 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20151230 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20160406 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20161202 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingSource |
VIKINGv20170715 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20160909 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vikingZY_selJ_SourceRemeasurement |
VIKINGZYSELJv20170124 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCDR2 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCDR3 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCDR4 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCDR5 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20110816 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20110909 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20120126 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20121128 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20130304 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20130805 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20140428 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20140903 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20150309 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20151218 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20160311 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20160822 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20170109 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20170411 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20171101 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20180702 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20181120 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20191212 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20210708 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20230816 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource |
VMCv20240226 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcSource, vmcSynopticSource |
VMCDR1 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcdeepSource |
VMCDEEPv20240506 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vmcdeepSource, vmcdeepSynopticSource |
VMCDEEPv20230713 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vvvSource |
VVVDR2 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vvvSource |
VVVDR5 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vvvSource |
VVVv20100531 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vvvSource |
VVVv20110718 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vvvSource, vvvSynopticSource |
VVVDR1 |
Probability that the source is a star |
real |
4 |
|
|
stat |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pStar |
vvvxSource |
VVVXDR1 |
Probability that the source is a star |
real |
4 |
|
|
stat.probability |
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code: Flag | Meaning | Probability (%) | | | Star | Galaxy | Noise | Saturated | -9 | Saturated | 0.0 | 0.0 | 5.0 | 95.0 | -3 | Probable galaxy | 25.0 | 70.0 | 5.0 | 0.0 | -2 | Probable star | 70.0 | 25.0 | 5.0 | 0.0 | -1 | Star | 90.0 | 5.0 | 5.0 | 0.0 | 0 | Noise | 5.0 | 5.0 | 90.0 | 0.0 | +1 | Galaxy | 5.0 | 90.0 | 5.0 | 0.0 | Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation. |
pts_key |
twomass_psc |
TWOMASS |
A unique identification number for the PSC source. |
int |
4 |
|
|
meta.id |
pts_key |
twomass_xsc |
TWOMASS |
key to point source data DB record. |
int |
4 |
|
|
meta.id |
publicDb |
Release |
SHARKSv20210222 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
SHARKSv20210421 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VHSv20201209 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VHSv20231101 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VHSv20240731 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VMCDEEPv20230713 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VMCDEEPv20240506 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VMCDR5 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VMCv20191212 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VMCv20210708 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VMCv20230816 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VMCv20240226 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VVVDR5 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
publicDb |
Release |
VVVXDR1 |
the name of the SQL Server database containing the public release |
varchar |
128 |
|
NONE |
?? |
pUnk |
vmcMLClassificationCatalogue |
VMCv20240226 |
Probability of the source being classed as Unknown. {catalogue TType keyword: Unk} |
float |
8 |
|
|
|
pUnkErr |
vmcMLClassificationCatalogue |
VMCv20240226 |
Error on probability of the source being classed as Unknown. {catalogue TType keyword: Unk_err} |
float |
8 |
|
|
|
pv21 |
CurrentAstrometry |
SHARKSv20210222 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
SHARKSv20210421 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
ULTRAVISTADR4 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSDR1 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSDR2 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSDR3 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSDR4 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSDR5 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSDR6 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSv20120926 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSv20130417 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSv20140409 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSv20150108 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VHSv20160114 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSv20160507 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSv20170630 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSv20180419 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSv20201209 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSv20231101 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VHSv20240731 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VIDEODR2 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIDEODR3 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIDEODR4 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIDEODR5 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VIDEOv20100513 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIDEOv20111208 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGDR2 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGDR3 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGDR4 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGv20110714 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGv20111019 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGv20130417 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGv20140402 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VIKINGv20150421 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
|
pv21 |
CurrentAstrometry |
VIKINGv20151230 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VIKINGv20160406 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VIKINGv20161202 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VIKINGv20170715 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCDEEPv20230713 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCDEEPv20240506 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCDR1 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCDR2 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCDR3 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCDR4 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCDR5 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20110816 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20110909 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20120126 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20121128 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20130304 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20130805 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20140428 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20140903 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20150309 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VMCv20151218 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20160311 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20160822 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20170109 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20170411 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20171101 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20180702 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20181120 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20191212 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20210708 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20230816 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VMCv20240226 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VVVDR1 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VVVDR2 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VVVDR5 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VVVXDR1 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv21 |
CurrentAstrometry |
VVVv20100531 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
CurrentAstrometry |
VVVv20110718 |
Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv21 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Coefficient for r term (use only with ZPN projection) |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
SHARKSv20210222 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
SHARKSv20210421 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
ULTRAVISTADR4 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSDR1 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSDR2 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSDR3 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSDR4 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSDR5 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSDR6 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSv20120926 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSv20130417 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSv20140409 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSv20150108 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VHSv20160114 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSv20160507 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSv20170630 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSv20180419 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSv20201209 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSv20231101 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VHSv20240731 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VIDEODR2 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIDEODR3 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIDEODR4 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIDEODR5 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VIDEOv20100513 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIDEOv20111208 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGDR2 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGDR3 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGDR4 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGv20110714 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGv20111019 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGv20130417 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGv20140402 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VIKINGv20150421 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
|
pv22 |
CurrentAstrometry |
VIKINGv20151230 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VIKINGv20160406 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VIKINGv20161202 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VIKINGv20170715 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCDEEPv20230713 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCDEEPv20240506 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCDR1 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCDR2 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCDR3 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCDR4 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCDR5 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20110816 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20110909 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20120126 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20121128 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20130304 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20130805 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20140428 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20140903 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20150309 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VMCv20151218 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20160311 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20160822 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20170109 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20170411 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20171101 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20180702 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20181120 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20191212 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20210708 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20230816 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VMCv20240226 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VVVDR1 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VVVDR2 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VVVDR5 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VVVXDR1 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv22 |
CurrentAstrometry |
VVVv20100531 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
CurrentAstrometry |
VVVv20110718 |
Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv22 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Coefficient for r**2 term (use only with ZPN projection) |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
SHARKSv20210222 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
SHARKSv20210421 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
ULTRAVISTADR4 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSDR1 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSDR2 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSDR3 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSDR4 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSDR5 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSDR6 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSv20120926 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSv20130417 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSv20140409 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSv20150108 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VHSv20160114 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSv20160507 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSv20170630 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSv20180419 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSv20201209 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSv20231101 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VHSv20240731 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VIDEODR2 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIDEODR3 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIDEODR4 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIDEODR5 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VIDEOv20100513 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIDEOv20111208 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGDR2 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGDR3 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGDR4 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGv20110714 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGv20111019 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGv20130417 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGv20140402 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VIKINGv20150421 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
|
pv23 |
CurrentAstrometry |
VIKINGv20151230 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VIKINGv20160406 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VIKINGv20161202 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VIKINGv20170715 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCDEEPv20230713 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCDEEPv20240506 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCDR1 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCDR2 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCDR3 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCDR4 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCDR5 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20110816 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20110909 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20120126 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20121128 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20130304 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20130805 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20140428 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20140903 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20150309 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VMCv20151218 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20160311 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20160822 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20170109 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20170411 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20171101 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20180702 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20181120 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20191212 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20210708 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20230816 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VMCv20240226 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VVVDR1 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VVVDR2 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VVVDR5 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VVVXDR1 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv23 |
CurrentAstrometry |
VVVv20100531 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
CurrentAstrometry |
VVVv20110718 |
Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv23 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Coefficient for r**3 term (use only with ZPN projection) |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
SHARKSv20210222 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
SHARKSv20210421 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
ULTRAVISTADR4 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSDR1 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSDR2 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSDR3 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSDR4 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSDR5 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSDR6 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSv20120926 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSv20130417 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSv20140409 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSv20150108 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VHSv20160114 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSv20160507 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSv20170630 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSv20180419 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSv20201209 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSv20231101 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VHSv20240731 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VIDEODR2 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIDEODR3 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIDEODR4 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIDEODR5 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VIDEOv20100513 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIDEOv20111208 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGDR2 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGDR3 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGDR4 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGv20110714 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGv20111019 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGv20130417 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGv20140402 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VIKINGv20150421 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
|
pv24 |
CurrentAstrometry |
VIKINGv20151230 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VIKINGv20160406 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VIKINGv20161202 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VIKINGv20170715 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCDEEPv20230713 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCDEEPv20240506 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCDR1 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCDR2 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCDR3 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCDR4 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCDR5 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20110816 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20110909 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20120126 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20121128 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20130304 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20130805 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20140428 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20140903 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20150309 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VMCv20151218 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20160311 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20160822 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20170109 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20170411 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20171101 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20180702 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20181120 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20191212 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20210708 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20230816 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VMCv20240226 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VVVDR1 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VVVDR2 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VVVDR5 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VVVXDR1 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv24 |
CurrentAstrometry |
VVVv20100531 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
CurrentAstrometry |
VVVv20110718 |
Coefficient for r**4 term (use only with ZPN projection) {image extension keyword: PV2_4} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv24 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Coefficient for r**4 term (use only with ZPN projection) |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
SHARKSv20210222 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
SHARKSv20210421 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
ULTRAVISTADR4 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSDR1 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSDR2 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSDR3 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSDR4 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSDR5 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSDR6 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSv20120926 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSv20130417 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSv20140409 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSv20150108 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VHSv20160114 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSv20160507 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSv20170630 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSv20180419 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSv20201209 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSv20231101 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VHSv20240731 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VIDEODR2 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIDEODR3 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIDEODR4 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIDEODR5 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VIDEOv20100513 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIDEOv20111208 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGDR2 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGDR3 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGDR4 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGv20110714 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGv20111019 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGv20130417 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGv20140402 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VIKINGv20150421 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
|
pv25 |
CurrentAstrometry |
VIKINGv20151230 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VIKINGv20160406 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VIKINGv20161202 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VIKINGv20170715 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCDEEPv20230713 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCDEEPv20240506 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCDR1 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCDR2 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCDR3 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCDR4 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCDR5 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20110816 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20110909 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20120126 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20121128 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20130304 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20130805 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20140428 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20140903 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20150309 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VMCv20151218 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20160311 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20160822 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20170109 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20170411 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20171101 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20180702 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20181120 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20191212 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20210708 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20230816 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VMCv20240226 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VVVDR1 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VVVDR2 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VVVDR5 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VVVXDR1 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
stat.fit.param |
pv25 |
CurrentAstrometry |
VVVv20100531 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
CurrentAstrometry |
VVVv20110718 |
Coefficient for r**5 term (use only with ZPN projection) {image extension keyword: PV2_5} transformation from pixel to celestial co-ordinates |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pv25 |
sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry |
VSAQC |
Coefficient for r**5 term (use only with ZPN projection) |
float |
8 |
|
-0.9999995e9 |
pos.wcs.cdmatrix |
pxcntr |
twomass_psc |
TWOMASS |
The pts_key value of the nearest source in the PSC. |
int |
4 |
|
|
meta.number |
pxcntr |
twomass_xsc |
TWOMASS |
ext_key value of nearest XSC source. |
int |
4 |
|
|
meta.number |
pxpa |
twomass_psc, twomass_xsc |
TWOMASS |
The position angle on the sky of the vector from the source to the nearest neighbor in the PSC, in degrees East of North. |
smallint |
2 |
degrees |
|
pos.posAng |