M 
Name  Schema Table  Database  Description  Type  Length  Unit  Default Value  Unified Content Descriptor 
M1 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Number of detections for band 1 
int 
4 

9 

M1_1_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 1 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M1_1_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 1 Maximum likelihood 
real 
4 



M1_1_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 1 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M1_1_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 1 flux 
real 
4 
erg/cm**2/s 


M1_1_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 1 flux error 
real 
4 
erg/cm**2/s 


M1_1_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 1 Count rates 
real 
4 
counts/s 


M1_1_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 1 Count rates error 
real 
4 
counts/s 


M1_1_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 1 vignetting value. 
real 
4 



M1_2_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 2 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M1_2_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 2 Maximum likelihood 
real 
4 



M1_2_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 1 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M1_2_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 2 flux 
real 
4 
erg/cm**2/s 


M1_2_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 2 flux error 
real 
4 
erg/cm**2/s 


M1_2_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 2 Count rates 
real 
4 
counts/s 


M1_2_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 2 Count rates error 
real 
4 
counts/s 


M1_2_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 2 vignetting value. 
real 
4 



M1_3_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 3 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M1_3_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 3 Maximum likelihood 
real 
4 



M1_3_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 2 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M1_3_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 3 flux 
real 
4 
erg/cm**2/s 


M1_3_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 3 flux error 
real 
4 
erg/cm**2/s 


M1_3_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 3 Count rates 
real 
4 
counts/s 


M1_3_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 3 Count rates error 
real 
4 
counts/s 


M1_3_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 3 vignetting value. 
real 
4 



M1_4_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 4 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M1_4_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 4 Maximum likelihood 
real 
4 



M1_4_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 3 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M1_4_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 4 flux 
real 
4 
erg/cm**2/s 


M1_4_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 4 flux error 
real 
4 
erg/cm**2/s 


M1_4_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 4 Count rates 
real 
4 
counts/s 


M1_4_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 4 Count rates error 
real 
4 
counts/s 


M1_4_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 4 vignetting value. 
real 
4 



M1_5_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 5 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M1_5_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 5 Maximum likelihood 
real 
4 



M1_5_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 4 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M1_5_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 5 flux 
real 
4 
erg/cm**2/s 


M1_5_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 5 flux error 
real 
4 
erg/cm**2/s 


M1_5_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 5 Count rates 
real 
4 
counts/s 


M1_5_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 5 Count rates error 
real 
4 
counts/s 


M1_5_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 band 5 vignetting value. 
real 
4 



M1_8_CTS 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
Combined band source counts 
real 
4 
counts 


M1_8_CTS_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
Combined band source counts 1 σ error 
real 
4 
counts 


M1_8_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 8 Maximum likelihood 
real 
4 



M1_8_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 8 flux 
real 
4 
erg/cm**2/s 


M1_8_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 8 flux error 
real 
4 
erg/cm**2/s 


M1_8_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 8 Count rates 
real 
4 
counts/s 


M1_8_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 8 Count rates error 
real 
4 
counts/s 


M1_9_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 9 Maximum likelihood 
real 
4 



M1_9_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 9 flux 
real 
4 
erg/cm**2/s 


M1_9_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 9 flux error 
real 
4 
erg/cm**2/s 


M1_9_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 9 Count rates 
real 
4 
counts/s 


M1_9_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M1 band 9 Count rates error 
real 
4 
counts/s 


M1_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 M1 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 



M1_CHI2PROB 
xmm3dr4 
XMM 
The Chi² probability (based on the null hypothesis) that the source as detected by the M1 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 



M1_FILTER 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 
XMM 
M1 filter. The options are Thick, Medium, Thin1, and Open, depending on the efficiency of the optical blocking. 
varchar 
6 



M1_FILTER 
xmm3dr4 
XMM 
M1 filter. The options are Thick, Medium, Thin1, and Open, depending on the efficiency of the optical blocking. 
varchar 
50 



M1_FLAG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 
XMM 
M1 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 M1 the flags are all set to False (default). Flag 10 is not used. 
varchar 
12 



M1_FLAG 
xmm3dr4 
XMM 
M1 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 M1 the flags are all set to False (default). Flag 10 is not used. 
varchar 
50 



M1_FVAR 
xmm3dr4 
XMM 
The fractional excess variance measured in the MOS1 timeseries of the detection. Where multiple MOS1 exposures exist, it is for the one giving the largest probability of variability (M1_CHI2PROB). This quantity provides a measure of the amplitude of variability in the timeseries, above purely statistical fluctuations. 
float 
8 



M1_FVARERR 
xmm3dr4 
XMM 
The error on the fractional excess variance for the MOS1 timeseries of the detection (M1_FVAR). 
float 
8 



M1_HR1 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 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 



M1_HR1_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M1 hardness ratio between the bands 1 & 2 
real 
4 



M1_HR2 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 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 



M1_HR2_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M1 hardness ratio between the bands 2 & 3 
real 
4 



M1_HR3 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 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 



M1_HR3_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M1 hardness ratio between the bands 3 & 4 
real 
4 



M1_HR4 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 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 



M1_HR4_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M1 hardness ratio between the bands 4 & 5 
real 
4 



M1_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 



M1_OFFAX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 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 


M1_ONTIME 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M1 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 


M1_SUBMODE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 
XMM 
M1 observing mode. The options are full frame mode with the full FOV exposed, partial window mode with only parts of the central CCD exposed (in different submodes), and timing mode where the central CCD was not exposed ('Fast Uncompressed'). 
varchar 
16 



M1_SUBMODE 
xmm3dr4 
XMM 
M1 observing mode. The options are full frame mode with the full FOV exposed, partial window mode with only parts of the central CCD exposed (in different submodes), and timing mode where the central CCD was not exposed ('Fast Uncompressed'). 
varchar 
50 



M2 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Number of detections for band 2 
int 
4 

9 

M2_1_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 1 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M2_1_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 1 Maximum likelihood 
real 
4 



M2_1_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 5 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M2_1_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 1 flux 
real 
4 
erg/cm**2/s 


M2_1_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 1 flux error 
real 
4 
erg/cm**2/s 


M2_1_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 1 Count rates 
real 
4 
counts/s 


M2_1_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 1 Count rates error 
real 
4 
counts/s 


M2_1_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 1 vignetting value. 
real 
4 



M2_2_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 2 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M2_2_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 2 Maximum likelihood 
real 
4 



M2_2_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 2 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M2_2_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 2 flux 
real 
4 
erg/cm**2/s 


M2_2_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 2 flux error 
real 
4 
erg/cm**2/s 


M2_2_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 2 Count rates 
real 
4 
counts/s 


M2_2_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 2 Count rates error 
real 
4 
counts/s 


M2_2_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 2 vignetting value. 
real 
4 



M2_3_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 3 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M2_3_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 3 Maximum likelihood 
real 
4 



M2_3_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 3 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M2_3_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 3 flux 
real 
4 
erg/cm**2/s 


M2_3_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 3 flux error 
real 
4 
erg/cm**2/s 


M2_3_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 3 Count rates 
real 
4 
counts/s 


M2_3_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 3 Count rates error 
real 
4 
counts/s 


M2_3_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 3 vignetting value. 
real 
4 



M2_4_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 4 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M2_4_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 4 Maximum likelihood 
real 
4 



M2_4_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 4 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M2_4_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 4 flux 
real 
4 
erg/cm**2/s 


M2_4_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 4 flux error 
real 
4 
erg/cm**2/s 


M2_4_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 4 Count rates 
real 
4 
counts/s 


M2_4_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 4 Count rates error 
real 
4 
counts/s 


M2_4_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 4 vignetting value. 
real 
4 



M2_5_BG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 5 background map. Made using a 12 x 12 nodes spline fit on the sourcefree individualband images. 
real 
4 
counts/pixel 


M2_5_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 5 Maximum likelihood 
real 
4 



M2_5_EXP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 5 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps. The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individualband exposure maps. 
real 
4 
s 


M2_5_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 5 flux 
real 
4 
erg/cm**2/s 


M2_5_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 5 flux error 
real 
4 
erg/cm**2/s 


M2_5_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 5 Count rates 
real 
4 
counts/s 


M2_5_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 5 Count rates error 
real 
4 
counts/s 


M2_5_VIG 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 band 5 vignetting value. 
real 
4 



M2_8_CTS 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
Combined band source counts 
real 
4 
counts 


M2_8_CTS_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
Combined band source counts 1 σ error 
real 
4 
counts 


M2_8_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 8 Maximum likelihood 
real 
4 



M2_8_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 8 flux 
real 
4 
erg/cm**2/s 


M2_8_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 8 flux error 
real 
4 
erg/cm**2/s 


M2_8_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 8 Count rates 
real 
4 
counts/s 


M2_8_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 8 Count rates error 
real 
4 
counts/s 


M2_9_DET_ML 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 9 Maximum likelihood 
real 
4 



M2_9_FLUX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 9 flux 
real 
4 
erg/cm**2/s 


M2_9_FLUX_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 9 flux error 
real 
4 
erg/cm**2/s 


M2_9_RATE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 9 Count rates 
real 
4 
counts/s 


M2_9_RATE_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
M2 band 9 Count rates error 
real 
4 
counts/s 


M2_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 M2 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 



M2_CHI2PROB 
xmm3dr4 
XMM 
The Chi² probability (based on the null hypothesis) that the source as detected by the M2 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 



M2_FILTER 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 
XMM 
M2 filter. The options are Thick, Medium, Thin1, and Open, depending on the efficiency of the optical blocking. 
varchar 
6 



M2_FILTER 
xmm3dr4 
XMM 
M2 filter. The options are Thick, Medium, Thin1, and Open, depending on the efficiency of the optical blocking. 
varchar 
50 



M2_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 M2 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 M2 the flags are all set to False (default). Flag 10 is not used. 
varchar 
12 



M2_FLAG 
xmm3dr4 
XMM 
PN flag string made of the flags 1  12 (counted from left to right) for the M2 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 M2 the flags are all set to False (default). Flag 10 is not used. 
varchar 
50 



M2_FVAR 
xmm3dr4 
XMM 
The fractional excess variance measured in the MOS2 timeseries of the detection. Where multiple MOS2 exposures exist, it is for the one giving the largest probability of variability (M2_CHI2PROB). This quantity provides a measure of the amplitude of variability in the timeseries, above purely statistical fluctuations. 
float 
8 



M2_FVARERR 
xmm3dr4 
XMM 
The error on the fractional excess variance for the MOS2 timeseries of the detection (M2_FVAR). 
float 
8 



M2_HR1 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 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 



M2_HR1_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M2 hardness ratio between the bands 1 & 2 
real 
4 



M2_HR2 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 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 



M2_HR2_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M2 hardness ratio between the bands 2 & 3 
real 
4 



M2_HR3 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 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 



M2_HR3_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M2 hardness ratio between the bands 3 & 4 
real 
4 



M2_HR4 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 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 



M2_HR4_ERR 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The 1 σ error of the M2 hardness ratio between the bands 4 & 5 
real 
4 



M2_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 



M2_OFFAX 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 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 


M2_ONTIME 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
The M2 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 


M2_SUBMODE 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 
XMM 
M2 observing mode. The options are full frame mode with the full FOV exposed, partial window mode with only parts of the central CCD exposed (in different submodes), and timing mode where the central CCD was not exposed ('Fast Uncompressed'). 
varchar 
16 



M2_SUBMODE 
xmm3dr4 
XMM 
M2 observing mode. The options are full frame mode with the full FOV exposed, partial window mode with only parts of the central CCD exposed (in different submodes), and timing mode where the central CCD was not exposed ('Fast Uncompressed'). 
varchar 
50 



M3 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Number of detections for band 3 
int 
4 

9 

M3_6 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Number of detections for 3.6um IRAC (Band 1) 
int 
4 

9 

m3_6 
sage_lmcIracSource 
SPITZER 
Number of detections for band 3.6 
int 
4 



m3_6 
sage_smcIRACv1_5Source 
SPITZER 
Number of detections for 3.6um IRAC (Band 1) 
int 
4 



M4 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Number of detections for band 4 
int 
4 

9 

M4_5 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Number of detections for 4.5um IRAC (Band 2) 
int 
4 

9 

m4_5 
sage_lmcIracSource 
SPITZER 
Number of detections for band 4.5 
int 
4 



m4_5 
sage_smcIRACv1_5Source 
SPITZER 
Number of detections for 4.5um IRAC (Band 2) 
int 
4 



M5_8 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Number of detections for 5.8um IRAC (Band 3) 
int 
4 

9 

m5_8 
sage_lmcIracSource 
SPITZER 
Number of detections for band 5.8 
int 
4 



m5_8 
sage_smcIRACv1_5Source 
SPITZER 
Number of detections for 5.8um IRAC (Band 3) 
int 
4 



M8_0 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Number of detections for 8.0um IRAC (Band 4) 
int 
4 

9 

m8_0 
sage_lmcIracSource 
SPITZER 
Number of detections for band 8.0 
int 
4 



m8_0 
sage_smcIRACv1_5Source 
SPITZER 
Number of detections for 8.0um IRAC (Band 4) 
int 
4 



machoID 
ogle3LpvLmcSource, ogle3LpvSmcSource 
OGLE 
MACHO ID 
varchar 
14 


meta.id 
MAD_HRV 
ravedr5Source 
RAVE 
Median absolute deviation in HRV from 10 resampled spectra 
float 
8 
km/s 

stat.error;stat.median 
MAD_logg_K 
ravedr5Source 
RAVE 
Median absolute deviation of surface gravity from 10 resampled spectra 
float 
8 
dex 

stat.error;stat.median;phys.gravity 
MAD_Met_K 
ravedr5Source 
RAVE 
Median absolute deviation in Met_K from 10 resampled spectra 
float 
8 
dex 

stat.error;stat.median;phys.abund.Z 
MAD_Teff_K 
ravedr5Source 
RAVE 
Median absolute deviation in Teff_K from 10 resampled spectra 
float 
8 
K 

stat.error;stat.median;phys.temperature.effective 
mag 
vvvParallaxCatalogue, vvvProperMotionCatalogue 
VVVDR4 
Median of Ks band aperMag2 measurements from all epochs in the pawprint set {catalogue TType keyword: mag} 
real 
4 

999999500.0 

mag1 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Magnitude in IRAC band 1 
real 
4 
mag 
99.999 

mag160 
sage_lmcMips160Source 
SPITZER 
160um magnitude 
float 
8 
mag 


mag1_err 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
1sigma mag error (IRAC band 1) 
real 
4 
mag 
99.999 

mag2 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Magnitude in IRAC band 2 
real 
4 
mag 
99.999 

mag24 
sage_lmcMips24Source 
SPITZER 
24um magnitude 
float 
8 
mag 


mag2_err 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
1sigma mag error (IRAC band 2) 
real 
4 
mag 
99.999 

mag3 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Magnitude in IRAC band 3 
real 
4 
mag 
99.999 

mag3_6 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
3.6um IRAC (Band 1) magnitude 
real 
4 
mag 
99.999 

mag3_6 
sage_lmcIracSource 
SPITZER 
3.6um magnitude 
real 
4 
mag 


mag3_6 
sage_smcIRACv1_5Source 
SPITZER 
3.6um IRAC (Band 1) magnitude 
real 
4 
mag 


mag3_6_err 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
3.6um IRAC (Band 1) 1 sigma error 
real 
4 
mag 
99.999 

mag3_err 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
1sigma mag error (IRAC band 3) 
real 
4 
mag 
99.999 

mag4 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Magnitude in IRAC band 4 
real 
4 
mag 
99.999 

mag4_5 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
4.5um IRAC (Band 2) magnitude 
real 
4 
mag 
99.999 

mag4_5 
sage_lmcIracSource 
SPITZER 
4.5um magnitude 
real 
4 
mag 


mag4_5 
sage_smcIRACv1_5Source 
SPITZER 
4.5um IRAC (Band 1) magnitude 
real 
4 
mag 


mag4_5_err 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
4.5um IRAC (Band 2) 1 sigma error 
real 
4 
mag 
99.999 

mag4_err 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
1sigma mag error (IRAC band 4) 
real 
4 
mag 
99.999 

mag5_8 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
5.8um IRAC (Band 3) magnitude 
real 
4 
mag 
99.999 

mag5_8 
sage_lmcIracSource 
SPITZER 
5.8um magnitude 
real 
4 
mag 


mag5_8 
sage_smcIRACv1_5Source 
SPITZER 
5.8um IRAC (Band 1) magnitude 
real 
4 
mag 


mag5_8_err 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
5.8um IRAC (Band 3) 1 sigma error 
real 
4 
mag 
99.999 

mag70 
sage_lmcMips70Source 
SPITZER 
70um magnitude 
float 
8 
mag 


mag8_0 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
8.0um IRAC (Band 4) magnitude 
real 
4 
mag 
99.999 

mag8_0 
sage_lmcIracSource 
SPITZER 
8.0um magnitude 
real 
4 
mag 


mag8_0 
sage_smcIRACv1_5Source 
SPITZER 
8.0um IRAC (Band 1) magnitude 
real 
4 
mag 


mag8_0_err 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
8.0um IRAC (Band 4) 1 sigma error 
real 
4 
mag 
99.999 

MAG_1 
agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss 
SIXDF 
supplied magnitude 1 
real 
4 
mag 


MAG_1 
supercos 
SIXDF 
Bj mag, SuperCOSMOS magnitudes generated from a revised calibration , done at the time of input catalogue preparation, tuned to the mag range of interest. 
real 
4 
mag 


MAG_1 
twomass 
SIXDF 
supplied magnitude 
real 
4 
mag 


MAG_2 
agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss 
SIXDF 
supplied magnitude 2 
real 
4 
mag 


MAG_2 
supercos 
SIXDF 
R mag, SuperCOSMOS magnitudes generated from a revised calibration, done at the time of input catalogue preparation, tuned to the mag range of interest. 
real 
4 
mag 


MAG_2 
twomass 
SIXDF 
supplied magnitude 
real 
4 
mag 


MAG_3 
agntwomass 
SIXDF 
supplied magnitude 3 
real 
4 



MAG_AUTO 
mgcDetection 
MGC 
Kronlike elliptical aperture magnitude 
real 
4 
mag 


MAG_AUTO_DC 
mgcDetection 
MGC 
MAG_AUTO corrected for extinction 
real 
4 
mag 


mag_bj 
igsl_source 
GAIADR1 
B magnitude measure (GSC23 system) 
real 
4 
mag 

phot.mag;em.opt.B 
mag_bj_error 
igsl_source 
GAIADR1 
Error in B magnitude measure 
real 
4 
mag 

stat.error;phot.mag;em.opt.B 
MAG_ERR 
mgcDetection 
MGC 
Error for B_MGC 
real 
4 
mag 


mag_g 
igsl_source 
GAIADR1 
G magnitude estimate 
real 
4 
mag 

phot.mag;em.opt 
mag_g_error 
igsl_source 
GAIADR1 
Error on G magnitude estimate 
real 
4 
mag 

stat.error;phot.mag;em.opt 
mag_grvs 
igsl_source 
GAIADR1 
RVS G magnitude estimate 
real 
4 
mag 

phot.mag;em.opt 
mag_grvs_error 
igsl_source 
GAIADR1 
Error on RVS G magnitude estimate 
real 
4 
mag 

stat.error;phot.mag;em.opt 
MAG_ISO 
mgcDetection 
MGC 
Isophotal magnitude 
real 
4 
mag 


MAG_ISO_DC 
mgcDetection 
MGC 
MAG_ISO corrected for extinction 
real 
4 
mag 


MAG_ISOCOR 
mgcDetection 
MGC 
Gaussian corrected isophotal magnitude 
real 
4 
mag 


MAG_ISOCOR_DC 
mgcDetection 
MGC 
MAG_ISOCOR corrected for extinction 
real 
4 
mag 


mag_rf 
igsl_source 
GAIADR1 
R magnitude measure (GSC23 system) 
real 
4 
mag 

phot.mag;em.opt.R 
mag_rf_error 
igsl_source 
GAIADR1 
Error in R magnitude measure 
real 
4 
mag 

stat.error;phot.mag;em.opt.R 
magB 
eros2LMCSource, eros2SMCSource, erosLMCSource, erosSMCSource 
EROS 
Mean magnitude in blue channel 
real 
4 



magH 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
2MASS AllSky PSC H Band magnitude 
real 
4 
mag 
99.999 

magH 
sage_lmcIracSource 
SPITZER 
H band magnitude 
real 
4 
mag 


magH 
sage_smcIRACv1_5Source 
SPITZER 
2MASS AllSky PSC H band magnitude 
real 
4 
mag 


magH_err 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
2MASS AllSky PSC H Band 1 sigma error 
real 
4 
mag 
99.999 

magI 
ogle3LpvLmcSource, ogle3LpvSmcSource, ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource 
OGLE 
Intensity mean Iband magnitude 
real 
4 
mag 

phot.mag 
magJ 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
2MASS AllSky PSC J Band magnitude 
real 
4 
mag 
99.999 

magJ 
sage_lmcIracSource 
SPITZER 
J band magnitude 
real 
4 
mag 


magJ 
sage_smcIRACv1_5Source 
SPITZER 
2MASS AllSky PSC J band magnitude 
real 
4 
mag 


magJ_err 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
2MASS AllSky PSC J Band 1 sigma error 
real 
4 
mag 
99.999 

magK 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
2MASS AllSky PSC Ks Band magnitude 
real 
4 
mag 
99.999 

magK 
sage_lmcIracSource 
SPITZER 
K band magnitude 
real 
4 
mag 


magK 
sage_smcIRACv1_5Source 
SPITZER 
2MASS AllSky PSC K band magnitude 
real 
4 
mag 


magKs_err 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
2MASS AllSky PSC Ks Band 1 sigma error 
real 
4 
mag 
99.999 

MAGL15 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude 
float 
8 
mag 
99.999 

MAGL24 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude 
float 
8 
mag 
99.999 

MAGN3 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude 
float 
8 
mag 
99.999 

magR 
eros2LMCSource, eros2SMCSource, erosLMCSource, erosSMCSource 
EROS 
Mean magnitude in red channel 
real 
4 



MAGS11 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude 
float 
8 
mag 
99.999 

MAGS7 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude 
float 
8 
mag 
99.999 

magV 
ogle3LpvLmcSource, ogle3LpvSmcSource, ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource 
OGLE 
Intensity mean Vband magnitude 
real 
4 
mag 

phot.mag 
mainImageID 
Multiframe 
ULTRAVISTADR4 
UID of frame that this auxilliary image is related to 
bigint 
8 

99999999 
obs.field 
maj 
first08Jul16Source, firstSource, firstSource12Feb16 
FIRST 
major axes derived from the elliptical Gaussian model for the source after deconvolution. 
real 
4 
arcsec 

phys.angSize.smajAxis 
MajAxis 
combo17CDFSSource 
COMBO17 
major axis (as observed in 1" seeing) 
real 
4 
arcsec 


majAxis 
nvssSource 
NVSS 
Fitted (deconvolved) major axis of radio source 
real 
4 
arcsec 

phys.angSize.smajAxis 
major 
iras_psc 
IRAS 
Uncertainty ellipse major axis 
smallint 
2 
arcsec 

stat.error 
mapID 
CombinedFilters 
ULTRAVISTADR4 
the unique mapID 
int 
4 


meta.id 
mapID 
MapApertureIDsultraVistaMapLc, MapApertureIDsultravistaDual, ultravistaRemeasMergeLog 
ULTRAVISTADR4 
UID of matchedaperture product 
int 
4 


meta_id 
mapID 
MapApertureIDsvikingZY_selJ 
VIKINGZYSELJv20170124 
UID of matchedaperture product 
int 
4 


meta_id 
mapID 
MapApertureIDsvikingZY_selJ, vikingZY_selJ_RemeasMergeLog 
VIKINGZYSELJv20160909 
UID of matchedaperture product 
int 
4 


meta_id 
mapID 
MapFilterLupt 
ULTRAVISTADR4 
the unique mapID ID 
int 
4 


meta.id;meta.main 
mapID 
MapFrameStatus 
ULTRAVISTADR4 
UID of matchedaperture product 
int 
4 

99999999 
meta_id_N/_99999999 
mapID 
MapSurveyTables, RequiredMapAverages 
ULTRAVISTADR4 
the UID of the matchedaperture product 
int 
4 


meta.id 
mapID 
RequiredMatchedApertureProduct 
ULTRAVISTADR4 
the UID of the matchedaperture product 
int 
4 


meta.main;meta.id 
mapID 
ThreeDimExtinctionMaps 
EXTINCT 
UID of the map 
tinyint 
1 


meta.id;meta.main 
mapID 
ultravistaMapRemeasAver 
ULTRAVISTADR4 
UID of the matchedaperture product that this remeasurement is part of, see RequiredMatchedApertureProduct 
int 
4 



mapID 
ultravistaMapRemeasurement 
ULTRAVISTADR4 
UID of the matchedaperture product that this remeasurement is part of, see RequiredMatchedApertureProduct {catalogue extension keyword: MAPID} 
int 
4 



mapID 
vikingMapRemeasAver 
VIKINGZYSELJv20160909 
UID of the matchedaperture product that this remeasurement is part of, see RequiredMatchedApertureProduct 
int 
4 



mapID 
vikingMapRemeasAver 
VIKINGZYSELJv20170124 
UID of the matchedaperture product that this remeasurement is part of, see RequiredMatchedApertureProduct 
int 
4 



mapID 
vikingMapRemeasurement 
VIKINGZYSELJv20160909 
UID of the matchedaperture product that this remeasurement is part of, see RequiredMatchedApertureProduct {catalogue extension keyword: MAPID} 
int 
4 



mapID 
vikingMapRemeasurement 
VIKINGZYSELJv20170124 
UID of the matchedaperture product that this remeasurement is part of, see RequiredMatchedApertureProduct {catalogue extension keyword: MAPID} 
int 
4 



mapID 
vvvBulgeExtMapCoords 
EXTINCT 
UID of the map 
tinyint 
1 


meta.id 
mapName 
ThreeDimExtinctionMaps 
EXTINCT 
Name of map 
varchar 
16 


meta.id 
mapTableID 
MapApertureIDsultraVistaMapLc, MapApertureIDsultravistaDual 
ULTRAVISTADR4 
The UID of the survey, table information in MapSurveyTables 
int 
4 



mapTableID 
MapApertureIDsvikingZY_selJ 
VIKINGZYSELJv20160909 
The UID of the survey, table information in MapSurveyTables 
int 
4 



mapTableID 
MapApertureIDsvikingZY_selJ 
VIKINGZYSELJv20170124 
The UID of the survey, table information in MapSurveyTables 
int 
4 



mapTableID 
MapSurveyTables 
ULTRAVISTADR4 
running number referring to the surveyID and tableID 
int 
4 



mapType 
RequiredMatchedApertureProduct 
ULTRAVISTADR4 
type of matchedaperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3) 
smallint 
2 



maskID 
Multiframe 
ULTRAVISTADR4 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSDR1 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSDR2 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSDR3 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSDR4 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSDR5 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSDR6 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20120926 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20130417 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20140409 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20150108 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20160114 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20160507 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20170630 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VHSv20180419 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIDEODR2 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIDEODR3 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIDEODR4 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIDEODR5 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIDEOv20100513 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIDEOv20111208 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGDR2 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGDR3 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGDR4 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20110714 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20111019 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20130417 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20140402 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20150421 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20151230 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20160406 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20161202 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20170715 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VIKINGv20181012 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCDR1 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCDR2 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCDR3 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCDR4 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20110816 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20110909 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20120126 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20121128 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20130304 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20130805 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20140428 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20140903 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20150309 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20151218 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20160311 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20160822 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20170109 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20170411 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20171101 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20180702 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VMCv20181120 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VVVDR1 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VVVDR2 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VVVDR4 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VVVv20100531 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
Multiframe 
VVVv20110718 
UID of library object mask frame {image extension keyword: CIR_OPM} 
bigint 
8 

99999999 
obs.field 
maskID 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
UID of library object mask frame 
bigint 
8 

99999999 
obs.field 
masktype 
Multiframe 
ULTRAVISTADR4 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSDR1 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSDR2 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSDR3 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSDR4 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSDR5 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSDR6 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20120926 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20130417 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20140409 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20150108 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20160114 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20160507 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20170630 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VHSv20180419 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIDEODR2 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIDEODR3 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIDEODR4 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIDEODR5 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIDEOv20100513 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIDEOv20111208 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGDR2 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGDR3 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGDR4 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20110714 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20111019 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20130417 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20140402 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20150421 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20151230 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20160406 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20161202 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20170715 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VIKINGv20181012 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCDR1 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCDR2 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCDR3 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCDR4 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20110816 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20110909 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20120126 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20121128 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20130304 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20130805 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20140428 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20140903 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20150309 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20151218 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20160311 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20160822 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20170109 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20170411 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20171101 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20180702 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VMCv20181120 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VVVDR1 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VVVDR2 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VVVDR4 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VVVv20100531 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
Multiframe 
VVVv20110718 
Mask type {image primary HDU keyword: MASKTYPE} 
tinyint 
1 

0 

masktype 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Mask type 
tinyint 
1 

0 

masterObjID 
ultravistaSourceNeighbours, ultravistaSourceXDR13PhotoObj, ultravistaSourceXDR13PhotoObjAll, ultravistaSourceXDetection, ultravistaSourceXGDR2gaia_source, ultravistaSourceXSSASource, ultravistaSourceXallwise_sc, ultravistaSourceXravedr5Source, ultravistaSourceXtwomass_psc, ultravistaSourceXtwompzPhotoz, ultravistaSourceXwiseScosPhotoz, ultravistaSourceXwise_allskysc 
ULTRAVISTADR4 
The unique ID in ultravistaSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours 
VHSDR3 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours 
VHSv20130417 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours 
VHSv20140409 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours 
VHSv20150108 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours 
VHSv20160114 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours 
VHSv20160507 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours 
VHSv20170630 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours, vhsSourceXAtlasDR1Source, vhsSourceXwise_allskysc 
VHSDR2 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours, vhsSourceXDR10lasSource, vhsSourceXDR13PhotoObj, vhsSourceXDR13PhotoObjAll, vhsSourceXatlasDR3 
VHSv20180419 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours, vhsSourceXDR5lasSource, vhsSourceXDR7PhotoObj, vhsSourceXDR7PhotoObjAll, vhsSourceXDR8lasSource, vhsSourceXPawPrints, vhsSourceXSSASource, vhsSourceXSegueDR6PhotoObj, vhsSourceXSegueDR6PhotoObjAll, vhsSourceXStripe82PhotoObjAll, vhsSourceXfirstSource, vhsSourceXiras_psc, vhsSourceXmgcDetection, vhsSourceXnvssSource, vhsSourceXrosat_bsc, vhsSourceXrosat_fsc, vhsSourceXtwomass_psc, vhsSourceXtwomass_sixx2_xsc, vhsSourceXtwomass_xsc, vhsSourceXtwoxmm, vhsSourceXwise_prelimsc 
VHSDR1 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours, vhsSourceXGDR1gaia_source, vhsSourceXGDR1tgas_source 
VHSDR6 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours, vhsSourceXallwise_sc, vhsSourceXatlasDR1 
VHSDR4 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours, vhsSourceXfirstSource12Feb16 
VHSv20120926 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vhsSourceNeighbours, vhsSourceXtwompzPhotoz, vhsSourceXwiseScosPhotoz 
VHSDR5 
The unique ID in vhsSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
videoSourceNeighbours 
VIDEODR5 
The unique ID in videoSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
videoSourceNeighbours 
VIDEOv20100513 
The unique ID in videoSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
videoSourceNeighbours 
VIDEOv20111208 
The unique ID in videoSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
videoSourceNeighbours, videoSourceXDetection, videoSourceXSSASource, videoSourceXStripe82PhotoObjAll, videoSourceXtwomass_psc, videoSourceXtwomass_xsc, videoSourceXwise_prelimsc 
VIDEODR2 
The unique ID in videoSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
videoSourceNeighbours, videoSourceXallwise_sc 
VIDEODR4 
The unique ID in videoSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
videoSourceNeighbours, videoSourceXwise_allskysc 
VIDEODR3 
The unique ID in videoSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours 
VIKINGDR4 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours 
VIKINGv20110714 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours 
VIKINGv20111019 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours 
VIKINGv20130417 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours 
VIKINGv20140402 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours 
VIKINGv20151230 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours 
VIKINGv20160406 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours, vikingSourceXAtlasDR1Source, vikingSourceXwise_allskysc 
VIKINGDR3 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours, vikingSourceXDR5lasSource, vikingSourceXDR7PhotoObj, vikingSourceXDR7PhotoObjAll, vikingSourceXDetection, vikingSourceXSSASource, vikingSourceXStripe82PhotoObjAll, vikingSourceXgrs_ngpSource, vikingSourceXgrs_ranSource, vikingSourceXgrs_sgpSource, vikingSourceXmgcDetection, vikingSourceXtwomass_psc, vikingSourceXtwomass_xsc, vikingSourceXwise_prelimsc 
VIKINGDR2 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours, vikingSourceXGDR1gaia_source, vikingSourceXGDR1tgas_source 
VIKINGv20170715 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours, vikingSourceXGDR1tgas_source, vikingSourceXGDR2gaia_source, vikingSourceXallwise_sc, vikingSourceXfirstSource 
VIKINGv20181012 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours, vikingSourceXallwise_sc 
VIKINGv20150421 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vikingSourceNeighbours, vikingSourceXtwompzPhotoz, vikingSourceXwiseScosPhotoz 
VIKINGv20161202 
The unique ID in vikingSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcPsfCatalogueXGDR1gaia_source 
VMCv20171101 
The unique ID in vmcPsfCatalogue (=psfID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcPsfCatalogueXGDR1gaia_source, vmcPsfCatalogueXGDR1tgas_source, vmcPsfCatalogueXSSASource, vmcPsfCatalogueXakari_lmc_psa_v1, vmcPsfCatalogueXakari_lmc_psc_v1, vmcPsfCatalogueXallwise_sc, vmcPsfCatalogueXdenisDR3Source, vmcPsfCatalogueXeros2LMCSource, vmcPsfCatalogueXeros2SMCSource, vmcPsfCatalogueXerosLMCSource, vmcPsfCatalogueXerosSMCSource, vmcPsfCatalogueXmachoLMCSource, vmcPsfCatalogueXmachoSMCSource, vmcPsfCatalogueXmcps_lmcSource, vmcPsfCatalogueXmcps_smcSource, vmcPsfCatalogueXogle3LpvLmcSource, vmcPsfCatalogueXogle3LpvSmcSource, vmcPsfCatalogueXogle4CepLmcSource, vmcPsfCatalogueXogle4CepSmcSource, vmcPsfCatalogueXogle4RRLyrLmcSource, vmcPsfCatalogueXogle4RRLyrSmcSource, vmcPsfCatalogueXsage_lmcIracSource, vmcPsfCatalogueXsage_lmcMips160Source, vmcPsfCatalogueXsage_lmcMips24Source, vmcPsfCatalogueXsage_lmcMips70Source, vmcPsfCatalogueXspitzer_smcSource, vmcPsfCatalogueXtwomass_psc, vmcPsfCatalogueXtwomass_sixx2_psc, vmcPsfCatalogueXtwomass_sixx2_xsc, vmcPsfCatalogueXtwomass_xsc, vmcPsfCatalogueXtwompzPhotoz, vmcPsfCatalogueXwiseScosPhotoz, vmcPsfCatalogueXwise_allskysc 
VMCv20170411 
The unique ID in vmcPsfCatalogue (=psfID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcPsfDetectionsXGDR1gaia_source 
VMCv20181120 
The unique ID in vmcPsfDetections (=psfID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcPsfDetectionsXGDR1gaia_source, vmcPsfDetectionsXGDR1tgas_source, vmcPsfDetectionsXGDR2gaia_source, vmcPsfDetectionsXSSASource, vmcPsfDetectionsXakari_lmc_psa_v1, vmcPsfDetectionsXakari_lmc_psc_v1, vmcPsfDetectionsXallwise_sc, vmcPsfDetectionsXdenisDR3Source, vmcPsfDetectionsXeros2LMCSource, vmcPsfDetectionsXeros2SMCSource, vmcPsfDetectionsXerosLMCSource, vmcPsfDetectionsXerosSMCSource, vmcPsfDetectionsXmachoLMCSource, vmcPsfDetectionsXmachoSMCSource, vmcPsfDetectionsXmcps_lmcSource, vmcPsfDetectionsXmcps_smcSource, vmcPsfDetectionsXogle3LpvLmcSource, vmcPsfDetectionsXogle3LpvSmcSource, vmcPsfDetectionsXogle4CepLmcSource, vmcPsfDetectionsXogle4CepSmcSource, vmcPsfDetectionsXogle4RRLyrLmcSource, vmcPsfDetectionsXogle4RRLyrSmcSource, vmcPsfDetectionsXravedr5Source, vmcPsfDetectionsXsage_lmcIracSource, vmcPsfDetectionsXsage_lmcMips160Source, vmcPsfDetectionsXsage_lmcMips24Source, vmcPsfDetectionsXsage_lmcMips70Source, vmcPsfDetectionsXspitzer_smcSource, vmcPsfDetectionsXtwomass_psc, vmcPsfDetectionsXtwomass_sixx2_psc, vmcPsfDetectionsXtwomass_sixx2_xsc, vmcPsfDetectionsXtwomass_xsc, vmcPsfDetectionsXtwompzPhotoz, vmcPsfDetectionsXwiseScosPhotoz, vmcPsfDetectionsXwise_allskysc 
VMCv20180702 
The unique ID in vmcPsfDetections (=psfID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcPsfSourceXGDR1gaia_source 
VMCv20181120 
The unique ID in vmcPsfSource (=psfSourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcPsfSourceXGDR1gaia_source, vmcPsfSourceXGDR1tgas_source, vmcPsfSourceXGDR2gaia_source, vmcPsfSourceXSSASource, vmcPsfSourceXakari_lmc_psa_v1, vmcPsfSourceXakari_lmc_psc_v1, vmcPsfSourceXallwise_sc, vmcPsfSourceXdenisDR3Source, vmcPsfSourceXeros2LMCSource, vmcPsfSourceXeros2SMCSource, vmcPsfSourceXerosLMCSource, vmcPsfSourceXerosSMCSource, vmcPsfSourceXmachoLMCSource, vmcPsfSourceXmachoSMCSource, vmcPsfSourceXmcps_lmcSource, vmcPsfSourceXmcps_smcSource, vmcPsfSourceXogle3LpvLmcSource, vmcPsfSourceXogle3LpvSmcSource, vmcPsfSourceXogle4CepLmcSource, vmcPsfSourceXogle4CepSmcSource, vmcPsfSourceXogle4RRLyrLmcSource, vmcPsfSourceXogle4RRLyrSmcSource, vmcPsfSourceXravedr5Source, vmcPsfSourceXsage_lmcIracSource, vmcPsfSourceXsage_lmcMips160Source, vmcPsfSourceXsage_lmcMips24Source, vmcPsfSourceXsage_lmcMips70Source, vmcPsfSourceXspitzer_smcSource, vmcPsfSourceXtwomass_psc, vmcPsfSourceXtwomass_sixx2_psc, vmcPsfSourceXtwomass_sixx2_xsc, vmcPsfSourceXtwomass_xsc, vmcPsfSourceXtwompzPhotoz, vmcPsfSourceXwiseScosPhotoz, vmcPsfSourceXwise_allskysc 
VMCv20180702 
The unique ID in vmcPsfSource (=psfSourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20110816 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20110909 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20120126 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20121128 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20130304 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20130805 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20140903 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20150309 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20151218 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20160311 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20170109 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20171101 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours 
VMCv20181120 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXGDR1gaia_source, vmcSourceXGDR1tgas_source 
VMCv20170411 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXGDR2gaia_source, vmcSourceXPsfDetections, vmcSourceXPsfSource, vmcSourceXravedr5Source 
VMCv20180702 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXPsfCatalogue, vmcSourceXVariablesType, vmcSourceXeros2LMCSource, vmcSourceXeros2SMCSource, vmcSourceXwise_allskysc 
VMCDR2 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXSSASource, vmcSourceXSynopticSource, vmcSourceXdenisDR3Source, vmcSourceXerosLMCSource, vmcSourceXerosSMCSource, vmcSourceXmachoLMCSource, vmcSourceXmachoSMCSource, vmcSourceXmcps_lmcSource, vmcSourceXmcps_smcSource, vmcSourceXsage_lmcIracSource, vmcSourceXsage_lmcMips160Source, vmcSourceXsage_lmcMips24Source, vmcSourceXsage_lmcMips70Source, vmcSourceXspitzer_smcSource, vmcSourceXtwomass_psc, vmcSourceXtwomass_sixx2_psc, vmcSourceXtwomass_sixx2_xsc, vmcSourceXtwomass_xsc, vmcSourceXwise_prelimsc 
VMCDR1 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXakari_lmc_psa_v1, vmcSourceXakari_lmc_psc_v1 
VMCDR3 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXallwise_sc 
VMCDR4 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXogle3LpvLmcSource, vmcSourceXogle3LpvSmcSource, vmcSourceXogle4CepLmcSource, vmcSourceXogle4CepSmcSource, vmcSourceXogle4RRLyrLmcSource, vmcSourceXogle4RRLyrSmcSource, vmcSourceXtwompzPhotoz, vmcSourceXwiseScosPhotoz 
VMCv20160822 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vmcSourceNeighbours, vmcSourceXxmm3dr4 
VMCv20140428 
The unique ID in vmcSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vvvPsfDaophotJKsSourceXDR8gpsSource, vvvPsfDaophotJKsSourceXSSASource, vvvPsfDaophotJKsSourceXallwise_sc, vvvPsfDaophotJKsSourceXgaia_source, vvvPsfDaophotJKsSourceXglimpse1_hrc, vvvPsfDaophotJKsSourceXglimpse1_mca, vvvPsfDaophotJKsSourceXglimpse2_hrc, vvvPsfDaophotJKsSourceXglimpse2_mca, vvvPsfDaophotJKsSourceXiras_psc, vvvPsfDaophotJKsSourceXtgas_source, vvvPsfDaophotJKsSourceXtwomass_psc, vvvPsfDaophotJKsSourceXtwomass_sixx2_psc, vvvPsfDaophotJKsSourceXtwomass_sixx2_xsc, vvvPsfDaophotJKsSourceXtwomass_xsc, vvvPsfDaophotJKsSourceXwise_allskysc, vvvPsfDaophotJKsSourceXxmm3dr4 
VVVDR4 
The unique ID in vvvPsfDaophotJKsSource (=psfID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vvvPsfDophotZYJHKsSourceXDR8gpsSource, vvvPsfDophotZYJHKsSourceXSSASource, vvvPsfDophotZYJHKsSourceXallwise_sc, vvvPsfDophotZYJHKsSourceXgaia_source, vvvPsfDophotZYJHKsSourceXglimpse1_hrc, vvvPsfDophotZYJHKsSourceXglimpse1_mca, vvvPsfDophotZYJHKsSourceXglimpse2_hrc, vvvPsfDophotZYJHKsSourceXglimpse2_mca, vvvPsfDophotZYJHKsSourceXiras_psc, vvvPsfDophotZYJHKsSourceXtgas_source, vvvPsfDophotZYJHKsSourceXtwomass_psc, vvvPsfDophotZYJHKsSourceXtwomass_sixx2_psc, vvvPsfDophotZYJHKsSourceXtwomass_sixx2_xsc, vvvPsfDophotZYJHKsSourceXtwomass_xsc, vvvPsfDophotZYJHKsSourceXwise_allskysc, vvvPsfDophotZYJHKsSourceXxmm3dr4 
VVVDR4 
The unique ID in vvvPsfDophotZYJHKsSource (=psfID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vvvSourceNeighbours 
VVVv20110718 
The unique ID in vvvSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vvvSourceNeighbours, vvvSourceXDR4gpsSource, vvvSourceXDetection, vvvSourceXSSASource, vvvSourceXSynopticSource, vvvSourceXglimpse_hrc_inter, vvvSourceXglimpse_mca_inter, vvvSourceXiras_psc, vvvSourceXtwomass_psc, vvvSourceXtwomass_sixx2_xsc, vvvSourceXtwomass_xsc 
VVVDR1 
The unique ID in vvvSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vvvSourceNeighbours, vvvSourceXgpsSource 
VVVv20100531 
The unique ID in vvvSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vvvSourceNeighbours, vvvSourceXwise_allskysc 
VVVDR2 
The unique ID in vvvSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
masterObjID 
vvvSourceXDR8gpsSource, vvvSourceXGDR1gaia_source, vvvSourceXGDR1tgas_source, vvvSourceXParallaxCatalogue, vvvSourceXProperMotionCatalogue, vvvSourceXPsfDaophotJKsSource, vvvSourceXPsfDophotZYJHKsSource, vvvSourceXSynopticSource, vvvSourceXglimpse1_hrc, vvvSourceXglimpse1_mca, vvvSourceXglimpse2_hrc, vvvSourceXglimpse2_mca, vvvSourceXwise_allskysc, vvvSourceXxmm3dr4 
VVVDR4 
The unique ID in vvvSource (=sourceID) 
bigint 
8 


meta.id;meta.main 
MATCH_1XMM 
twoxmm, twoxmm_v1_2 
XMM 
The IAU name of the 1XMM source ID matched within radius of 3 arcsec and using the closest candidate. 
varchar 
21 



MATCH_2XMMP 
twoxmm, twoxmm_v1_2 
XMM 
The IAU name of the 2XMMp source ID matched within radius of 3" and using the closest candidate. 
varchar 
22 



MATCH_DR 
spectra 
SIXDF 
position match error (arcsec) 
float 
8 
arcsec 


matched_observations 
gaia_source 
GAIADR2 
The number of observations matched to this source 
smallint 
2 


meta.number 
matched_observations 
gaia_source, tgas_source 
GAIADR1 
Amount of observations matched to this source 
smallint 
2 


meta.number 
MatchFlag_2MASS 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
3 


meta.code 
MatchFlag_ALLWISE 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
3 


meta.code 
MatchFlag_APASSDR9 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
5 


meta.code 
MatchFlag_DENIS 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
5 


meta.code 
MatchFlag_PPMXL 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
3 


meta.code 
MatchFlag_TGAS 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
5 


meta.code 
MatchFlag_TYCHO2 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
5 


meta.code 
MatchFlag_UCAC4 
ravedr5Source 
RAVE 
Crossmatch quality flag (Note 7, DR5) 
varchar 
3 


meta.code 
MatchFlag_USNOB1 
ravedr5Source 
RAVE 
Crossmatch quality flag 
varchar 
3 


meta.code 
MATL15 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
the number of matching with L15 merging the N3S7S11 list with the L15 list 
tinyint 
1 

0 

MATL24 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
the number of matching with L24 merging the N3S7S11L15 list with the L24 list 
tinyint 
1 

0 

MATS11 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
the number of matching with S11 merging the N3S7 list with the S11 list 
tinyint 
1 

0 

MATS7 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
the number of matching with S7 merging the N3 list with the S7 list 
tinyint 
1 

0 

maxDec 
CurrentAstrometry 
ULTRAVISTADR4 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSDR1 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSDR2 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSDR3 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSDR4 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSDR5 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSDR6 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20120926 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20130417 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20140409 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20150108 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20160114 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20160507 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20170630 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VHSv20180419 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIDEODR2 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIDEODR3 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIDEODR4 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIDEODR5 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIDEOv20100513 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIDEOv20111208 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGDR2 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGDR3 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGDR4 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20110714 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20111019 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20130417 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20140402 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20150421 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20151230 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20160406 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20161202 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20170715 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VIKINGv20181012 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCDR1 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCDR2 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCDR3 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCDR4 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20110816 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20110909 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20120126 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20121128 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20130304 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20130805 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20140428 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20140903 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20150309 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20151218 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20160311 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20160822 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20170109 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20170411 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20171101 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20180702 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VMCv20181120 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VVVDR1 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VVVDR2 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VVVDR4 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VVVv20100531 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
CurrentAstrometry 
VVVv20110718 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxDec 
ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry 
VSAQC 
The maximum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
maxGalLat 
ThreeDimExtinctionMaps 
EXTINCT 
Maximum Galactic Latitude 
float 
8 
Degrees 

stat.max;pos.galactic.lat 
maxGalLong 
ThreeDimExtinctionMaps 
EXTINCT 
Maximum Galactic Longitude 
float 
8 
Degrees 

stat.max;pos.galactic.lon 
maximum 
phot_variable_time_series_g_fov_statistical_parameters 
GAIADR1 
Maximum magnitude of the Gband time series 
float 
8 
mag 

phot.mag;stat.max 
maxJitSize 
Multiframe 
ULTRAVISTADR4 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSDR1 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSDR2 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSDR3 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSDR4 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSDR5 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSDR6 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20120926 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20130417 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20140409 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20150108 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20160114 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20160507 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20170630 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VHSv20180419 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIDEODR2 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIDEODR3 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIDEODR4 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIDEODR5 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIDEOv20111208 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGDR2 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGDR3 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGDR4 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20110714 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20111019 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20130417 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20140402 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20150421 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20151230 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20160406 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20161202 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20170715 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VIKINGv20181012 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCDR1 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCDR2 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCDR3 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCDR4 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20110816 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20110909 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20120126 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20121128 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20130304 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20130805 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20140428 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20140903 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20150309 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20151218 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20160311 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20160822 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20170109 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20170411 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20171101 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20180702 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VMCv20181120 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VVVDR1 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VVVDR2 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VVVDR4 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
Multiframe 
VVVv20110718 
SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} 
real 
4 
arcsec 
0.9999995e9 

maxJitSize 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
SADT maximum jitter size 
real 
4 
arcsec 
0.9999995e9 

maxMoonFli 
Multiframe 
ULTRAVISTADR4 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSDR1 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSDR2 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSDR3 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSDR4 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSDR5 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSDR6 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20120926 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20130417 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20140409 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20150108 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20160114 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20160507 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20170630 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VHSv20180419 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIDEODR2 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIDEODR3 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIDEODR4 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIDEODR5 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIDEOv20111208 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGDR2 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGDR3 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGDR4 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20110714 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20111019 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20130417 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20140402 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20150421 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20151230 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20160406 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20161202 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20170715 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VIKINGv20181012 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCDR1 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCDR2 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCDR3 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCDR4 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20110816 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20110909 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20120126 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20121128 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20130304 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20130805 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20140428 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20140903 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20150309 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20151218 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20160311 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20160822 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20170109 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20170411 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20171101 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20180702 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VMCv20181120 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VVVDR1 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VVVDR2 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VVVDR4 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
Multiframe 
VVVv20110718 
Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} 
real 
4 

0.9999995e9 

maxMoonFli 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Requested maximum fractional lunar illumination 
real 
4 

0.9999995e9 

maxPllx 
ultravistaVariability 
ULTRAVISTADR4 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
videoVariability 
VIDEODR2 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
videoVariability 
VIDEODR3 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
videoVariability 
VIDEODR4 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
videoVariability 
VIDEODR5 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
videoVariability 
VIDEOv20100513 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
videoVariability 
VIDEOv20111208 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGDR2 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGDR3 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGDR4 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20110714 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20111019 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20130417 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20140402 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20150421 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20151230 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20160406 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20161202 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20170715 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vikingVariability 
VIKINGv20181012 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCDR1 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCDR2 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCDR3 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCDR4 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20110816 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20110909 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20120126 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20121128 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20130304 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20130805 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20140428 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20140903 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20150309 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20151218 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20160311 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20160822 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20170109 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20170411 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20171101 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20180702 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vmcVariability 
VMCv20181120 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vvvVariability 
VVVDR1 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vvvVariability 
VVVDR2 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vvvVariability 
VVVDR4 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.max 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vvvVariability 
VVVv20100531 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxPllx 
vvvVariability 
VVVv20110718 
Upper limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
maxRa 
CurrentAstrometry 
ULTRAVISTADR4 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSDR1 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSDR2 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSDR3 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSDR4 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSDR5 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSDR6 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20120926 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20130417 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20140409 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20150108 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20160114 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20160507 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20170630 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VHSv20180419 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIDEODR2 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIDEODR3 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIDEODR4 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIDEODR5 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIDEOv20100513 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIDEOv20111208 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGDR2 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGDR3 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGDR4 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20110714 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20111019 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20130417 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20140402 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20150421 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20151230 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20160406 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20161202 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20170715 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VIKINGv20181012 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCDR1 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCDR2 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCDR3 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCDR4 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20110816 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20110909 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20120126 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20121128 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20130304 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20130805 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20140428 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20140903 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20150309 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20151218 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20160311 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20160822 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20170109 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20170411 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20171101 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20180702 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VMCv20181120 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VVVDR1 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VVVDR2 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VVVDR4 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VVVv20100531 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
CurrentAstrometry 
VVVv20110718 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
maxRa 
ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry 
VSAQC 
The maximum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
MC_class 
combo17CDFSSource 
COMBO17 
multicolour class: "Star"=stars (colour of star, stellar shape), "WDwarf"=WD/BHB/sdB star (colour of WD/BHB/sdB, stellar shape), "Galaxy"=galaxies (colour of galaxy, shape irrelevant), "Galaxy (Star?)"=most likely galaxy at z<0.15 (but overlap in colour space with stars), "Galaxy (Uncl!)"=colour undecided (statistically almost always a galaxy), "QSO"=QSOs (colour of QSO, stellar shape), "QSO (Gal?)"=colour of QSOs, extended shape (usually Seyfert 1), "Strange Objects"=very strange spectrum (very unusual intrinsic spectrum or strong photometric artifacts or uncorrected strong variability) 
varchar 
15 



MC_z 
combo17CDFSSource 
COMBO17 
mean redshift in distribution of p(z) 
real 
4 



MC_z2 
combo17CDFSSource 
COMBO17 
alternative redshift if p(z) bimodal 
real 
4 



MC_z_ml 
combo17CDFSSource 
COMBO17 
peak of redshift distribution p(z) 
real 
4 



Mcor_I 
denisDR3Source 
DENIS 
Mean correlation to PSF in I band [0,1] 
float 
8 



Mcor_J 
denisDR3Source 
DENIS 
Mean correlation to PSF in J band [0,1] 
float 
8 



Mcor_K 
denisDR3Source 
DENIS 
Mean correlation to PSF in K band [0,1] 
float 
8 



mean 
phot_variable_time_series_g_fov_statistical_parameters 
GAIADR1 
Mean magnitude of the Gband time series 
float 
8 
mag 

phot.mag;stat.mean 
mean_obs_time 
phot_variable_time_series_g_fov_statistical_parameters 
GAIADR1 
Mean observation time (with respect to T0) of Gband time series 
float 
8 
days 

time.epoch;stat.mean 
mean_varpi_factor_al 
gaia_source 
GAIADR2 
Mean parallax factor AlongScan 
real 
4 


stat.mean;pos.parallax;arith.factor 
meanMjdObs 
vmcSynopticMergeLog 
VMCDR1 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCDR2 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCDR3 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCDR4 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20110816 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20110909 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20120126 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20121128 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20130304 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20130805 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20140428 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20140903 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20150309 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20151218 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20160311 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20160822 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20170109 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20170411 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20171101 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20180702 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vmcSynopticMergeLog 
VMCv20181120 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vvvSynopticMergeLog 
VVVDR1 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vvvSynopticMergeLog 
VVVDR2 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
meanMjdObs 
vvvSynopticMergeLog 
VVVDR4 
Mean modified julian date of frameset. 
float 
8 
days 
0.9999995e9 
time.epoch 
median 
phot_variable_time_series_g_fov_statistical_parameters 
GAIADR1 
Median magnitude of the Gband time series 
float 
8 
mag 

phot.mag;stat.median 
median_absolute_deviation 
phot_variable_time_series_g_fov_statistical_parameters 
GAIADR1 
Median Absolute Deviation of the Gband time series values 
float 
8 
mag 

phot.mag;stat.value 
medPa 
MultiframeDetector 
ULTRAVISTADR4 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSDR1 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSDR2 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSDR3 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSDR4 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSDR5 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSDR6 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20120926 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20130417 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20140409 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20150108 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20160114 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20160507 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20170630 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VHSv20180419 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIDEODR2 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIDEODR3 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIDEODR4 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIDEODR5 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIDEOv20111208 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGDR2 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGDR3 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGDR4 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20110714 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20111019 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20130417 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20140402 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20150421 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20151230 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20160406 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20161202 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20170715 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VIKINGv20181012 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCDR1 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCDR2 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCDR3 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCDR4 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20110816 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20110909 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20120126 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20121128 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20130304 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20130805 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20140428 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20140903 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20150309 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20151218 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20160311 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20160822 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20170109 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20170411 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20171101 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20180702 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VMCv20181120 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VVVDR1 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VVVDR2 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VVVDR4 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
MultiframeDetector 
VVVv20110718 
[deg] Mean PA from N to E {catalogue extension keyword: MED_PA} 
real 
4 

0.9999995e9 

medPa 
ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector 
VSAQC 
[deg] Mean PA from N to E 
real 
4 

0.9999995e9 

mergedClass 
ultravistaSource 
ULTRAVISTADR4 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
ultravistaSourceRemeasurement 
ULTRAVISTADR4 
Class flag based on remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vhsSource 
VHSDR1 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSDR2 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSDR3 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSDR4 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSDR5 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSDR6 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20120926 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20130417 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20140409 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20150108 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20160114 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20160507 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20170630 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSource 
VHSv20180419 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vhsSourceRemeasurement 
VHSDR1 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
videoSource 
VIDEODR2 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
videoSource 
VIDEODR3 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
videoSource 
VIDEODR4 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
videoSource 
VIDEODR5 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
videoSource 
VIDEOv20100513 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
videoSource 
VIDEOv20111208 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
videoSourceRemeasurement 
VIDEOv20100513 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vikingSource 
VIKINGDR2 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGDR3 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGDR4 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20110714 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20111019 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20130417 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20140402 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20150421 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20151230 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20160406 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20161202 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20170715 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSource 
VIKINGv20181012 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vikingSourceRemeasurement 
VIKINGv20110714 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vikingSourceRemeasurement 
VIKINGv20111019 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vikingZY_selJ_SourceRemeasurement 
VIKINGZYSELJv20160909 
Class flag based on remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vikingZY_selJ_SourceRemeasurement 
VIKINGZYSELJv20170124 
Class flag based on remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vmcSource 
VMCDR2 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCDR3 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCDR4 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20110816 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20110909 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20120126 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20121128 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20130304 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20130805 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20140428 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20140903 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20150309 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20151218 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20160311 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20160822 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20170109 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20170411 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20171101 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20180702 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource 
VMCv20181120 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSource, vmcSynopticSource 
VMCDR1 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vmcSourceRemeasurement 
VMCv20110816 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vmcSourceRemeasurement 
VMCv20110909 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vvvSource 
VVVDR2 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vvvSource 
VVVDR4 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vvvSource 
VVVv20100531 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vvvSource 
VVVv20110718 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vvvSource, vvvSynopticSource 
VVVDR1 
Class flag from available measurements (101239=galaxynoisestellarprobableStarprobableGalaxysaturated) 
smallint 
2 


meta.code 
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, selfconsistent probability values for a given classification code: Flag  Meaning  Probability (%)    Star  Galaxy  Noise  Saturated  9  Saturated  0.0  0.0  5.0  95.0  3  Probable galaxy  25.0  70.0  5.0  0.0  2  Probable star  70.0  25.0  5.0  0.0  1  Star  90.0  5.0  5.0  0.0  0  Noise  5.0  5.0  90.0  0.0  +1  Galaxy  5.0  90.0  5.0  0.0  Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(class_{k})=Π_{i}P(class_{k})_{i} / Σ_{k}Π_{i}P(class_{k})_{i} where class_{k} is one of stargalaxynoisesaturated, and i denotes the i^{th} single detection passband measurement available (the nonzero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in JHK as 12+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=2). An additional decision rule enforces mergedClass=9 (saturated) when any individual classification flag indicates saturation. 
mergedClass 
vvvSourceRemeasurement 
VVVv20100531 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClass 
vvvSourceRemeasurement 
VVVv20110718 
Class flag based on list remeasurement prescription 
smallint 
2 


meta.code 
mergedClassStat 
ultravistaSource 
ULTRAVISTADR4 
Merged SExtractor classification statistic 
real 
4 

0.9999995e9 
stat 
Inverse varianceweighted mean of the available individual passband SExtractor classification statistics *ClassStat. 
mergedClassStat 
ultravistaSourceRemeasurement 
ULTRAVISTADR4 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSDR1 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSDR2 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSDR3 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSDR4 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSDR5 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSDR6 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20120926 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20130417 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20140409 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20150108 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20160114 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20160507 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20170630 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vhsSource 
VHSv20180419 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
videoSource 
VIDEODR2 
Merged SExtractor classification statistic 
real 
4 

0.9999995e9 
stat 
Inverse varianceweighted mean of the available individual passband SExtractor classification statistics *ClassStat. 
mergedClassStat 
videoSource 
VIDEODR3 
Merged SExtractor classification statistic 
real 
4 

0.9999995e9 
stat 
Inverse varianceweighted mean of the available individual passband SExtractor classification statistics *ClassStat. 
mergedClassStat 
videoSource 
VIDEODR4 
Merged SExtractor classification statistic 
real 
4 

0.9999995e9 
stat 
Inverse varianceweighted mean of the available individual passband SExtractor classification statistics *ClassStat. 
mergedClassStat 
videoSource 
VIDEODR5 
Merged SExtractor classification statistic 
real 
4 

0.9999995e9 
stat 
Inverse varianceweighted mean of the available individual passband SExtractor classification statistics *ClassStat. 
mergedClassStat 
videoSource 
VIDEOv20100513 
Merged SExtractor classification statistic 
real 
4 

0.9999995e9 
stat 
Inverse varianceweighted mean of the available individual passband SExtractor classification statistics *ClassStat. 
mergedClassStat 
videoSource 
VIDEOv20111208 
Merged SExtractor classification statistic 
real 
4 

0.9999995e9 
stat 
Inverse varianceweighted mean of the available individual passband SExtractor classification statistics *ClassStat. 
mergedClassStat 
vikingSource 
VIKINGDR2 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGDR3 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGDR4 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20110714 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20111019 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20130417 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20140402 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20150421 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20151230 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20160406 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20161202 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20170715 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingSource 
VIKINGv20181012 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingZY_selJ_SourceRemeasurement 
VIKINGZYSELJv20160909 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vikingZY_selJ_SourceRemeasurement 
VIKINGZYSELJv20170124 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCDR2 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCDR3 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCDR4 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20110816 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20110909 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20120126 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20121128 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20130304 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20130805 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20140428 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20140903 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20150309 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20151218 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20160311 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20160822 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20170109 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20170411 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20171101 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20180702 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource 
VMCv20181120 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vmcSource, vmcSynopticSource 
VMCDR1 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vvvSource 
VVVDR2 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vvvSource 
VVVDR4 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vvvSource 
VVVv20100531 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vvvSource 
VVVv20110718 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergedClassStat 
vvvSource, vvvSynopticSource 
VVVDR1 
Merged N(0,1) stellarnessofprofile statistic 
real 
4 

0.9999995e9 
stat 
This profile classification statistic is a continuously distributed, Gaussian N(0,1) (i.e. zero mean, unit variance) statistic formed from the available individual classification statistics by averaging them and multiplying by N^{1/2} such that cuts on mergedClassStat result in completeness being independent of number of frames an object appears on, but with reliability improving with the number of frames. 
mergeDuration 
vvvSynopticMergeLog 
VVVDR2 
The difference in time between the start time of the last band and the first band. 
float 
8 
days 
0.9999995e9 

mergeDuration 
vvvSynopticMergeLog 
VVVDR4 
The difference in time between the start time of the last band and the first band. 
float 
8 
days 
0.9999995e9 

mergeLogTable 
Programme 
ULTRAVISTADR4 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSDR1 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSDR2 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSDR3 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSDR4 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSDR5 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSDR6 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSv20120926 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSv20130417 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSv20150108 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSv20160114 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSv20160507 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSv20170630 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VHSv20180419 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIDEODR2 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIDEODR3 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIDEODR4 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIDEODR5 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIDEOv20100513 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIDEOv20111208 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGDR2 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGDR3 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGDR4 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20110714 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20111019 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20130417 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20150421 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20151230 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20160406 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20161202 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20170715 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VIKINGv20181012 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCDR1 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCDR3 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCDR4 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20110816 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20110909 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20120126 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20121128 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20130304 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20130805 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20140428 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20140903 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20150309 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20151218 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20160311 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20160822 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20170109 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20170411 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20171101 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20180702 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VMCv20181120 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VSAQC 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VVVDR1 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VVVDR2 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VVVDR4 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VVVv20100531 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeLogTable 
Programme 
VVVv20110718 
Table name of curation log for source merging 
varchar 
64 


?? 
mergeSwVersion 
ultravistaMergeLog, ultravistaTileSet 
ULTRAVISTADR4 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
ultravistaRemeasMergeLog 
ULTRAVISTADR4 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.softwate 
mergeSwVersion 
vhsMergeLog 
VHSDR2 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vhsMergeLog 
VHSDR3 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSDR4 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSDR5 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSDR6 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20120926 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20130417 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20140409 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20150108 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20160114 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20160507 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20170630 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog 
VHSv20180419 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vhsMergeLog, vhsTileSet 
VHSDR1 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
videoMergeLog 
VIDEODR3 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
videoMergeLog 
VIDEODR4 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
videoMergeLog 
VIDEODR5 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
videoMergeLog 
VIDEOv20100513 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
videoMergeLog 
VIDEOv20111208 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
videoMergeLog, videoTileSet 
VIDEODR2 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGDR3 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGDR4 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20110714 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20111019 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20130417 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20140402 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20150421 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20151230 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20160406 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20161202 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20170715 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog 
VIKINGv20181012 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vikingMergeLog, vikingTileSet 
VIKINGDR2 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vikingZY_selJ_RemeasMergeLog 
VIKINGZYSELJv20160909 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vikingZY_selJ_RemeasMergeLog 
VIKINGZYSELJv20170124 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vmcMergeLog 
VMCDR2 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCDR3 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCDR4 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20110816 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20110909 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20120126 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20121128 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20130304 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20130805 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20140428 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20140903 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20150309 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20151218 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20160311 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20160822 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20170109 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20170411 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20171101 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20180702 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog 
VMCv20181120 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vmcMergeLog, vmcSynopticMergeLog, vmcTileSet 
VMCDR1 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vvvMergeLog 
VVVDR2 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vvvMergeLog 
VVVDR4 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
mergeSwVersion 
vvvMergeLog 
VVVv20100531 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vvvMergeLog 
VVVv20110718 
version number of the software used to merge the frames 
real 
4 


meta.software 
mergeSwVersion 
vvvMergeLog, vvvSynopticMergeLog, vvvTileSet 
VVVDR1 
version number of the software used to merge the frames 
real 
4 


meta.id;meta.software 
MERR2L15 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERR2L24 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERR2N3 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERR2S11 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERR2S7 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERRL15 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERRL24 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERRN3 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERRS11 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

MERRS7 
akari_lmc_psa_v1, akari_lmc_psc_v1 
AKARI 
magnitude error 
float 
8 
mag 
99.999 

Met_K 
ravedr5Source 
RAVE 
[m/H] 
float 
8 
dex 

phys.abund.Z 
Met_N_K 
ravedr5Source 
RAVE 
Calibrated metallicity [m/H] 
float 
8 
dex 

phys.abund.Z 
method 
RequiredDiffImage 
ULTRAVISTADR4 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSDR1 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSDR2 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSDR3 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSDR4 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSDR5 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSDR6 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSv20120926 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSv20130417 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSv20150108 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSv20160114 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSv20160507 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSv20170630 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VHSv20180419 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIDEODR2 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIDEODR3 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIDEODR4 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIDEODR5 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIDEOv20100513 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIDEOv20111208 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGDR2 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGDR3 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGDR4 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20110714 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20111019 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20130417 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20150421 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20151230 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20160406 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20161202 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20170715 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VIKINGv20181012 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCDR1 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCDR3 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCDR4 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20110816 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20110909 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20120126 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20121128 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20130304 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20130805 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20140428 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20140903 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20150309 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20151218 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20160311 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20160822 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20170109 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20170411 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20171101 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20180702 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VMCv20181120 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VVVDR1 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VVVDR2 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VVVDR4 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VVVv20100531 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
method 
RequiredDiffImage 
VVVv20110718 
CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) 
varchar 
64 


?? 
MF1 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Flux calc mathod flag for band 1 flux 
int 
4 

9 

MF2 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Flux calc mathod flag for band 2 flux 
int 
4 

9 

MF3 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Flux calc mathod flag for band 3 flux 
int 
4 

9 

MF3_6 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Flux calculation method flag 3.6um IRAC (Band 1) 
int 
4 

9 

mf3_6 
sage_lmcIracSource 
SPITZER 
Flux calc method for flag for band 3.6 
int 
4 



mf3_6 
sage_smcIRACv1_5Source 
SPITZER 
Flux calculation method flag 3.6um IRAC (Band 1) (see SAGESMC_IRAC_colDescriptions footnote 2) 
int 
4 



MF4 
glimpse_hrc_inter, glimpse_mca_inter 
GLIMPSE 
Flux calc mathod flag for band 4 flux 
int 
4 

9 

MF4_5 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Flux calculation method flag 4.5um IRAC (Band 2) 
int 
4 

9 

mf4_5 
sage_lmcIracSource 
SPITZER 
Flux calc method for flag for band 4.5 
int 
4 



mf4_5 
sage_smcIRACv1_5Source 
SPITZER 
Flux calculation method flag 4.5um IRAC (Band 2) (see SAGESMC_IRAC_colDescriptions footnote 2) 
int 
4 



MF5_8 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Flux calculation method flag 5.8um IRAC (Band 3) 
int 
4 

9 

mf5_8 
sage_lmcIracSource 
SPITZER 
Flux calc method for flag for band 5.8 
int 
4 



mf5_8 
sage_smcIRACv1_5Source 
SPITZER 
Flux calculation method flag 5.8um IRAC (Band 3) (see SAGESMC_IRAC_colDescriptions footnote 2) 
int 
4 



MF8_0 
glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca 
GLIMPSE 
Flux calculation method flag 8.0um IRAC (Band 4) 
int 
4 

9 

mf8_0 
sage_lmcIracSource 
SPITZER 
Flux calc method for flag for band 8.0 
int 
4 



mf8_0 
sage_smcIRACv1_5Source 
SPITZER 
Flux calculation method flag 8.0um IRAC (Band 4) (see SAGESMC_IRAC_colDescriptions footnote 2) 
int 
4 



mFlag 
rosat_bsc, rosat_fsc 
ROSAT 
source missed by SASS 
varchar 
1 


meta.code 
Mg 
ravedr5Source 
RAVE 
[Mg/H] abundance of Mg 
real 
4 
dex 

phys.abund.Z 
Mg_N 
ravedr5Source 
RAVE 
Number of used spectral lines in calc. of [Mg/H] 
smallint 
2 


meta.number 
MGC_B_KCORR 
mgcGalaxyStruct 
MGC 
MGC Bband Kcorrection 
real 
4 

0.000 

MGC_BEST_Z 
mgcGalaxyStruct 
MGC 
Best redshift 
real 
4 

9.99999 

MGC_BEST_ZQUAL 
mgcGalaxyStruct 
MGC 
Quality of best redshift (02 = BAD, 35=GOOD, 9=Not observed) 
tinyint 
1 

9 

MGC_HLR_TRUE 
mgcGalaxyStruct 
MGC 
Seeing corrected Half light radius 
real 
4 
arcsecs 


MGC_SEEING 
mgcGalaxyStruct 
MGC 
Seeing of MGC field 
real 
4 
arcsecs 


MGC_SPEC_TYPE 
mgcGalaxyStruct 
MGC 
Best spectral type fit from Poggianti (1998)sample (type+age, i.e., el150 = E/S0 15.0Gyrs) 
varchar 
8 

none 

MGCFN 
mgcDetection 
MGC 
MGC field number 
int 
4 



MGCID 
mgcBrightSpec, mgcDetection, mgcGalaxyStruct 
MGC 
MGC object ID 
bigint 
8 



MGCZ_ZHELIO 
mgcBrightSpec 
MGC 
MGCz heliocentric redshift 
real 
4 



MGCZ_ZQUAL 
mgcBrightSpec 
MGC 
MGCz redshift quality 
tinyint 
1 



mHcon 
iras_psc 
IRAS 
Possible number of HCONs 
tinyint 
1 


meta.number 
min 
first08Jul16Source, firstSource, firstSource12Feb16 
FIRST 
minor axes derived from the elliptical Gaussian model for the source after deconvolution. 
real 
4 
arcsec 

phys.angSize.sminAxis 
MinAxis 
combo17CDFSSource 
COMBO17 
minor axis (as observed in 1" seeing) 
real 
4 
arcsec 


minAxis 
nvssSource 
NVSS 
Fitted (deconvolved) minor axis of radio source 
real 
4 
arcsec 

phys.angSize.sminAxis 
minDec 
CurrentAstrometry 
ULTRAVISTADR4 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSDR1 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSDR2 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSDR3 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSDR4 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSDR5 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSDR6 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20120926 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20130417 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20140409 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20150108 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20160114 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20160507 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20170630 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VHSv20180419 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIDEODR2 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIDEODR3 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIDEODR4 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIDEODR5 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIDEOv20100513 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIDEOv20111208 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGDR2 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGDR3 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGDR4 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20110714 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20111019 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20130417 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20140402 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20150421 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20151230 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20160406 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20161202 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20170715 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VIKINGv20181012 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCDR1 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCDR2 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCDR3 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCDR4 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20110816 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20110909 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20120126 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20121128 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20130304 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20130805 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20140428 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20140903 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20150309 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20151218 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20160311 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20160822 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20170109 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20170411 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20171101 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20180702 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VMCv20181120 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VVVDR1 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VVVDR2 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VVVDR4 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VVVv20100531 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
CurrentAstrometry 
VVVv20110718 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minDec 
ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry 
VSAQC 
The minimum Dec (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.dec;meta.main 
minGalLat 
ThreeDimExtinctionMaps 
EXTINCT 
Minimum Galactic Latitude 
float 
8 
Degrees 

stat.min;pos.galactic.lat 
minGalLong 
ThreeDimExtinctionMaps 
EXTINCT 
Minimum Galactic Longitude 
float 
8 
Degrees 

stat.min;pos.galactic.lon 
minImageSize 
MultiframeDetector 
ULTRAVISTADR4 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSDR1 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSDR2 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSDR3 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSDR4 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSDR5 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSDR6 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20120926 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20130417 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20140409 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20150108 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20160114 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20160507 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20170630 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VHSv20180419 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIDEODR2 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIDEODR3 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIDEODR4 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIDEODR5 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIDEOv20100513 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIDEOv20111208 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGDR2 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGDR3 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGDR4 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20110714 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20111019 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20130417 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20140402 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20150421 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20151230 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20160406 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20161202 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20170715 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VIKINGv20181012 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCDR1 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCDR2 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCDR3 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCDR4 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20110816 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20110909 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20120126 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20121128 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20130304 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20130805 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20140428 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20140903 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20150309 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20151218 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20160311 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20160822 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20170109 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20170411 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20171101 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20180702 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VMCv20181120 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VVVDR1 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VVVDR2 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VVVDR4 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VVVv20100531 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
MultiframeDetector 
VVVv20110718 
Minimum size for images (pixels) {catalogue extension keyword: MINPIX} 
tinyint 
1 

0 

minImageSize 
ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector 
VSAQC 
Minimum size for images (pixels) 
tinyint 
1 

0 

minimum 
phot_variable_time_series_g_fov_statistical_parameters 
GAIADR1 
Minimum magnitude of the Gband time series 
float 
8 
mag 

phot.mag;stat.min 
minMoonDist 
Multiframe 
ULTRAVISTADR4 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSDR1 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSDR2 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSDR3 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSDR4 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSDR5 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSDR6 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20120926 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20130417 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20140409 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20150108 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20160114 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20160507 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20170630 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VHSv20180419 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIDEODR2 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIDEODR3 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIDEODR4 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIDEODR5 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIDEOv20111208 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGDR2 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGDR3 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGDR4 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20110714 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20111019 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20130417 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20140402 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20150421 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20151230 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20160406 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20161202 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20170715 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VIKINGv20181012 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCDR1 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCDR2 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCDR3 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCDR4 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20110816 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20110909 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20120126 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20121128 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20130304 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20130805 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20140428 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20140903 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20150309 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20151218 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20160311 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20160822 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20170109 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20170411 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20171101 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20180702 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VMCv20181120 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VVVDR1 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VVVDR2 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VVVDR4 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
Multiframe 
VVVv20110718 
Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} 
real 
4 
deg 
0.9999995e9 

minMoonDist 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Requested minimum angular distance to the moon 
real 
4 
deg 
0.9999995e9 

minor 
iras_psc 
IRAS 
Uncertainty ellipse minor axis 
smallint 
2 
arcsec 

stat.error 
minPllx 
ultravistaVariability 
ULTRAVISTADR4 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
videoVariability 
VIDEODR2 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
videoVariability 
VIDEODR3 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
videoVariability 
VIDEODR4 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
videoVariability 
VIDEODR5 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
videoVariability 
VIDEOv20100513 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
videoVariability 
VIDEOv20111208 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGDR2 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGDR3 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGDR4 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20110714 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20111019 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20130417 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20140402 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20150421 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20151230 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20160406 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20161202 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20170715 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vikingVariability 
VIKINGv20181012 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCDR1 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCDR2 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCDR3 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCDR4 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20110816 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20110909 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20120126 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20121128 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20130304 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20130805 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20140428 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20140903 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20150309 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20151218 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20160311 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20160822 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20170109 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20170411 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20171101 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20180702 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vmcVariability 
VMCv20181120 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vvvVariability 
VVVDR1 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vvvVariability 
VVVDR2 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vvvVariability 
VVVDR4 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 
pos.parallax;stat.min 
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vvvVariability 
VVVv20100531 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minPllx 
vvvVariability 
VVVv20110718 
Lower limit of 90% confidence interval for parallax measurement 
real 
4 
mas 
0.9999995e9 

The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian zaxis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chisquared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in nonsynoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table. 
minRa 
CurrentAstrometry 
ULTRAVISTADR4 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSDR1 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSDR2 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSDR3 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSDR4 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSDR5 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSDR6 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20120926 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20130417 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20140409 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20150108 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20160114 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20160507 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20170630 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VHSv20180419 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIDEODR2 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIDEODR3 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIDEODR4 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIDEODR5 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIDEOv20100513 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIDEOv20111208 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGDR2 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGDR3 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGDR4 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20110714 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20111019 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20130417 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20140402 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20150421 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20151230 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20160406 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20161202 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20170715 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VIKINGv20181012 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCDR1 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCDR2 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCDR3 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCDR4 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20110816 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20110909 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20120126 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20121128 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20130304 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20130805 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20140428 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20140903 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20150309 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20151218 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20160311 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20160822 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20170109 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20170411 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20171101 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20180702 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VMCv20181120 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VVVDR1 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VVVDR2 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VVVDR4 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VVVv20100531 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
CurrentAstrometry 
VVVv20110718 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
minRa 
ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry 
VSAQC 
The minimum RA (J2000) on the device 
float 
8 
Degrees 
0.9999995e9 
pos.eq.ra 
mjd 
ultravistaDetection, ultravistaMapRemeasurement 
ULTRAVISTADR4 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
ultravistaMapRemeasAver 
ULTRAVISTADR4 
Averaged Modified Julian Day of each detection 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSDR1 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vhsDetection 
VHSDR2 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vhsDetection 
VHSDR3 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSDR4 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSDR5 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSDR6 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20120926 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20130417 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20140409 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20150108 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20160114 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20160507 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20170630 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vhsDetection 
VHSv20180419 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
videoDetection 
VIDEODR2 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
videoDetection 
VIDEODR3 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
videoDetection 
VIDEODR4 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
videoDetection 
VIDEODR5 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
videoDetection 
VIDEOv20111208 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vikingDetection 
VIKINGDR2 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vikingDetection 
VIKINGDR3 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGDR4 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20110714 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vikingDetection 
VIKINGv20111019 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vikingDetection 
VIKINGv20130417 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20140402 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20150421 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20151230 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20160406 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20161202 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20170715 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingDetection 
VIKINGv20181012 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingMapRemeasAver 
VIKINGZYSELJv20160909 
Averaged Modified Julian Day of each detection 
float 
8 
day 

time.epoch 
mjd 
vikingMapRemeasAver 
VIKINGZYSELJv20170124 
Averaged Modified Julian Day of each detection 
float 
8 
day 

time.epoch 
mjd 
vikingMapRemeasurement 
VIKINGZYSELJv20160909 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vikingMapRemeasurement 
VIKINGZYSELJv20170124 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCDR1 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vmcDetection 
VMCDR2 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCDR3 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCDR4 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20110816 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vmcDetection 
VMCv20110909 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vmcDetection 
VMCv20120126 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 


mjd 
vmcDetection 
VMCv20121128 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20130304 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20130805 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20140428 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20140903 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20150309 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20151218 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20160311 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20160822 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20170109 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20170411 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20171101 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20180702 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vmcDetection 
VMCv20181120 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vvvDetection 
VVVDR1 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vvvDetection 
VVVDR2 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
mjd 
vvvDetection 
VVVDR4 
The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} 
float 
8 
day 

time.epoch 
MJD_FIRST 
xmm3dr4 
XMM 
The MJD start date (MJD_START) of the earliest observation of any constituent detection of the unique source. 
float 
8 



MJD_LAST 
xmm3dr4 
XMM 
The MJD end date (MJD_STOP) of the last observation of any constituent detection of the unique source. 
float 
8 



MJD_OBS 
ravedr5Source 
RAVE 
Modfied Julian Date 
float 
8 
day 

time 
MJD_START 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
Modified Julian Date (i.e., JD  2400000.5) of the start of the observation. 
float 
8 
days 


MJD_STOP 
twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 
XMM 
Modified Julian Date (i.e., JD  2400000.5) of the end of the observation. 
float 
8 



mjdEnd 
Multiframe 
ULTRAVISTADR4 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSDR1 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSDR2 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSDR3 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSDR4 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSDR5 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSDR6 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20120926 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20130417 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20140409 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20150108 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20160114 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20160507 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20170630 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VHSv20180419 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIDEODR2 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIDEODR3 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIDEODR4 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIDEODR5 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIDEOv20111208 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGDR2 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGDR3 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGDR4 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20110714 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20111019 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20130417 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20140402 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20150421 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20151230 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20160406 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20161202 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20170715 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VIKINGv20181012 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCDR1 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCDR2 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCDR3 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCDR4 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20110816 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20110909 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20120126 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20121128 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20130304 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20130805 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20140428 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20140903 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20150309 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20151218 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20160311 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20160822 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20170109 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20170411 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20171101 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20180702 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VMCv20181120 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VVVDR1 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VVVDR2 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VVVDR4 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
Multiframe 
VVVv20110718 
Modified Julian Date of the observation end {image primary HDU keyword: MJDEND} 
float 
8 

0.9999995e9 
time.epoch 
mjdEnd 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Modified Julian Date of the observation end 
float 
8 

0.9999995e9 
time.epoch 
mjdMean 
MultiframeDetector 
ULTRAVISTADR4 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSDR1 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSDR2 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSDR3 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSDR4 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSDR5 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSDR6 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20120926 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20130417 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20140409 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20150108 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20160114 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20160507 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20170630 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VHSv20180419 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIDEODR2 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIDEODR3 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIDEODR4 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIDEODR5 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIDEOv20111208 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGDR2 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGDR3 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGDR4 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20110714 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20111019 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20130417 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20140402 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20150421 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20151230 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20160406 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20161202 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20170715 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VIKINGv20181012 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCDR1 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCDR2 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCDR3 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCDR4 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20110816 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20110909 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20120126 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20121128 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20130304 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20130805 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20140428 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20140903 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20150309 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20151218 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20160311 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20160822 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20170109 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20170411 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20171101 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20180702 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VMCv20181120 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VVVDR1 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VVVDR2 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VVVDR4 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
MultiframeDetector 
VVVv20110718 
Mean MJD of all images comprising this image {catalogue extension keyword: MEANMJD} 
float 
8 
days 
0.9999995e9 

mjdMean 
ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector 
VSAQC 
Mean MJD of all images comprising this image 
float 
8 
days 
0.9999995e9 

mjdObs 
Multiframe 
ULTRAVISTADR4 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSDR1 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSDR2 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSDR3 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSDR4 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSDR5 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSDR6 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20120926 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20130417 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20140409 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20150108 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20160114 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20160507 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20170630 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VHSv20180419 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIDEODR2 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIDEODR3 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIDEODR4 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIDEODR5 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIDEOv20100513 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIDEOv20111208 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGDR2 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGDR3 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGDR4 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20110714 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20111019 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20130417 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20140402 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20150421 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20151230 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20160406 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20161202 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20170715 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VIKINGv20181012 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCDR1 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCDR2 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCDR3 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCDR4 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20110816 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20110909 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20120126 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20121128 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20130304 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20130805 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20140428 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20140903 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20150309 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20151218 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20160311 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20160822 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20170109 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20170411 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20171101 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20180702 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VMCv20181120 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VVVDR1 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VVVDR2 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VVVDR4 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VVVv20100531 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
Multiframe 
VVVv20110718 
Modified Julian Date of the observation start {image primary HDU keyword: MJDOBS} 
float 
8 

0.9999995e9 
time.epoch 
mjdObs 
ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe 
VSAQC 
Modified Julian Date of the observation start 
float 
8 

0.9999995e9 
time.epoch 
MJDOBS_R 
spectra 
SIXDF 
MJD of observation 
float 
8 
Julian days 


MJDOBS_V 
spectra 
SIXDF 
MJD of observation 
float 
8 
Julian days 


mmmNsky160 
sage_lmcMips160Source 
SPITZER 
Number of points used to determine the sky values for mmmSkymode160, mmmSigma160 and mmmSkew160 
float 
8 



mmmNsky24 
sage_lmcMips24Source 
SPITZER 
Number of points used to determine the sky values for mmmSkymode24, mmmSigma24 and mmmSkew24 
float 
8 



mmmNsky70 
sage_lmcMips70Source 
SPITZER 
Number of points used to determine the sky values for mmmSkymode70, mmmSigma70 and mmmSkew70 
float 
8 



mmmSigma160 
sage_lmcMips160Source 
SPITZER 
Scalar giving standard deviation of the peak in the sky histogram 
float 
8 



mmmSigma24 
sage_lmcMips24Source 
SPITZER 
Scalar giving standard deviation of the peak in the sky histogram 
float 
8 



mmmSigma70 
sage_lmcMips70Source 
SPITZER 
Scalar giving standard deviation of the peak in the sky histogram 
float 
8 



mmmSkew160 
sage_lmcMips160Source 
SPITZER 
Scalar giving skewness of the peak in the sky histogram 
float 
8 



mmmSkew24 
sage_lmcMips24Source 
SPITZER 
Scalar giving skewness of the peak in the sky histogram 
float 
8 



mmmSkew70 
sage_lmcMips70Source 
SPITZER 
Scalar giving skewness of the peak in the sky histogram 
float 
8 



mmmSkymode160 
sage_lmcMips160Source 
SPITZER 
Scalar giving estimated mode of the sky values 
float 
8 



mmmSkymode24 
sage_lmcMips24Source 
SPITZER 
Scalar giving estimated mode of the sky values 
float 
8 



mmmSkymode70 
sage_lmcMips70Source 
SPITZER 
Scalar giving estimated mode of the sky values 
float 
8 



modDate 
rosat_bsc, rosat_fsc 
ROSAT 
date when source properties were changed (MMDDYYYY) 
datetime 
8 
mmddyyyy 

time.epoch 
mode 
ogle4CepLmcSource, ogle4CepSmcSource 
OGLE 
Mode of pulsation 
varchar 
8 


meta.id.part 
mode_best_classification 
cepheid 
GAIADR1 
Best mode classification estimate out of {"FUNDAMENTAL", "FIRST_OVERTONE","SECOND_OVERTONE","UNDEFINED","NOT_APPLICABLE"} 
varchar 
16 


meta.code.class;src.class 
modelDistSecs 
ultravistaSourceXDetectionBestMatch 
ULTRAVISTADR4 
separation from expected position given astrometric model in ultravistaSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
videoSourceXDetectionBestMatch 
VIDEODR2 
separation from expected position given astrometric model in videoSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
videoSourceXDetectionBestMatch 
VIDEODR3 
separation from expected position given astrometric model in videoSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
videoSourceXDetectionBestMatch 
VIDEODR4 
separation from expected position given astrometric model in videoSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
videoSourceXDetectionBestMatch 
VIDEODR5 
separation from expected position given astrometric model in videoSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
videoSourceXDetectionBestMatch 
VIDEOv20100513 
separation from expected position given astrometric model in videoSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
videoSourceXDetectionBestMatch 
VIDEOv20111208 
separation from expected position given astrometric model in videoSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGDR2 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGDR3 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGDR4 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20110714 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20111019 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20130417 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20140402 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20150421 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20151230 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20160406 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20161202 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20170715 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vikingSourceXDetectionBestMatch 
VIKINGv20181012 
separation from expected position given astrometric model in vikingSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCDR1 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCDR2 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCDR3 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCDR4 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20110816 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20110909 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20120126 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20121128 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20130304 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20130805 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20140428 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20140903 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20150309 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20151218 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20160311 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20160822 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20170109 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20170411 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20171101 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20180702 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vmcSourceXSynopticSourceBestMatch 
VMCv20181120 
separation from expected position given astrometric model in vmcSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vvvSourceXDetectionBestMatch 
VVVDR2 
separation from expected position given astrometric model in vvvSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vvvSourceXDetectionBestMatch 
VVVDR4 
separation from expected position given astrometric model in vvvSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
modelDistSecs 
vvvSourceXDetectionBestMatch 
VVVv20100531 
separation from expected position given astrometric model in vvvSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vvvSourceXDetectionBestMatch 
VVVv20110718 
separation from expected position given astrometric model in vvvSource variability. 
real 
4 
arcsec 
0.9999995e9 

modelDistSecs 
vvvSourceXDetectionBestMatch, vvvSourceXSynopticSourceBestMatch 
VVVDR1 
separation from expected position given astrometric model in vvvSource variability. 
real 
4 
arcsec 
0.9999995e9 
pos.posAng 
moon_lev 
allwise_sc2 
WISE 
Scattered moonlight contamination flag. This is a fourcharacter string, one character per band, in which the value is an integer indicates the fraction of singleexposure frames on which the source was measured that were possibly contaminated by scattered moonlight. The value in each band is given by [ceiling(#frmmoon/#frames*10)], with a maximum value of 9, where #frmmoon is the number of affected frames and #frames is the total number of frames on which the source was measured. 
varchar 
4 



MORPH_TYPE 
mgcGalaxyStruct 
MGC 
SPD's EYEBALL morphology (1=E/S0, 2=Sabc, 3=Sd/Irr, 4=dE) 
tinyint 
1 

0 

morphClassFlag 
MultiframeDetector 
ULTRAVISTADR4 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSDR1 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSDR2 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSDR3 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSDR4 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSDR5 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSDR6 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20120926 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20130417 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20140409 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20150108 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20160114 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20160507 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20170630 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VHSv20180419 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIDEODR2 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIDEODR3 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIDEODR4 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIDEODR5 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIDEOv20100513 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIDEOv20111208 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGDR2 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGDR3 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGDR4 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20110714 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20111019 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20130417 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20140402 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20150421 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20151230 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20160406 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20161202 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20170715 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VIKINGv20181012 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCDR1 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCDR2 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCDR3 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCDR4 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20110816 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20110909 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20120126 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20121128 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20130304 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20130805 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20140428 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20140903 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20150309 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20151218 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20160311 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20160822 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20170109 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20170411 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20171101 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20180702 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VMCv20181120 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VVVDR1 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VVVDR2 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VVVDR4 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VVVv20100531 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
MultiframeDetector 
VVVv20110718 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword: CLASSIFD} 
tinyint 
1 

0 
meta.code 
morphClassFlag 
ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector 
VSAQC 
Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. 
tinyint 
1 

0 
meta.code 
mosaicSoft 
RequiredMosaicTopLevel 
ULTRAVISTADR4 
Mosaicing software (typically SWARP) 
varchar 
16 



mosaicTool 
Programme 
ULTRAVISTADR4 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSDR1 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSDR2 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSDR3 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSDR4 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSDR5 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSDR6 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSv20120926 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSv20130417 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSv20150108 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSv20160114 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSv20160507 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSv20170630 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VHSv20180419 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIDEODR2 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIDEODR3 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIDEODR4 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIDEODR5 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIDEOv20100513 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIDEOv20111208 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGDR2 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGDR3 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGDR4 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20110714 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20111019 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20130417 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20150421 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20151230 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20160406 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20161202 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20170715 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VIKINGv20181012 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCDR1 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCDR3 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCDR4 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20110816 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20110909 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20120126 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20121128 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20130304 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20130805 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20140428 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20140903 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20150309 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20151218 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20160311 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20160822 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20170109 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20170411 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20171101 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20180702 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VMCv20181120 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VSAQC 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VVVDR1 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VVVDR2 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VVVDR4 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VVVv20100531 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
mosaicTool 
Programme 
VVVv20110718 
Name of mosaicing tool to be used 
varchar 
8 

NONE 
?? 
motionModel 
ultravistaVarFrameSetInfo 
ULTRAVISTADR4 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
videoVarFrameSetInfo 
VIDEODR2 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 

Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
videoVarFrameSetInfo 
VIDEODR3 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
videoVarFrameSetInfo 
VIDEODR4 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
videoVarFrameSetInfo 
VIDEODR5 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
videoVarFrameSetInfo 
VIDEOv20100513 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 

Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
videoVarFrameSetInfo 
VIDEOv20111208 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 

Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGDR2 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 

Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGDR3 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGDR4 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20110714 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 

Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20111019 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 

Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20130417 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20140402 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20150421 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20151230 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20160406 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20161202 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to be stars with small values of proper motion and parallax. 
motionModel 
vikingVarFrameSetInfo 
VIKINGv20170715 
Motion model used to produce BestMatch table. Values: static;proper motion;proper motion and parallax. 
varchar 
32 

static 
meta.code.class 
Motion model for frameset in question. This can be static: all objects in the frameset are assumed to be stationary; proper motion: all objects in the frameset are fit for a linear proper motion; proper motion and parallax: all objects in the frameset are fit for a linear proper motion and a parallax. In all cases, objects are assumed to 