Home | Overview | Browser | Access | Login | Cookbook

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

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 sub-modes), 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 sub-modes), 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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 source-free individual-band 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 individual-band 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 sub-modes), 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 sub-modes), 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	smashdr2_source	SMASH	Instrumental magnitude from ALLFRAME or ALLSTAR (not both)	real	4
mag	vvvParallaxCatalogue, vvvProperMotionCatalogue	VVVDR5	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	Kron-like 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 All-Sky 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 All-Sky PSC H band magnitude	real	4	mag
magH_err	glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca	GLIMPSE	2MASS All-Sky PSC H Band 1 sigma error	real	4	mag	99.999
magI	ogle3LpvLmcSource, ogle3LpvSmcSource, ogle4CepLmcSource, ogle4CepSmcSource, ogle4RRLyrLmcSource, ogle4RRLyrSmcSource	OGLE	Intensity mean I-band magnitude	real	4	mag		phot.mag
magJ	glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca	GLIMPSE	2MASS All-Sky 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 All-Sky PSC J band magnitude	real	4	mag
magJ_err	glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca	GLIMPSE	2MASS All-Sky PSC J Band 1 sigma error	real	4	mag	99.999
magK	glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca	GLIMPSE	2MASS All-Sky 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 All-Sky PSC K band magnitude	real	4	mag
magKs_err	glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca	GLIMPSE	2MASS All-Sky 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 V-band magnitude	real	4	mag		phot.mag
mainImageID	Multiframe	SHARKSv20210222	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	SHARKSv20210421	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	ULTRAVISTADR4	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VHSv20201209	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VMCDEEPv20230713	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VMCDR5	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VMCv20191212	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VMCv20210708	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VMCv20230816	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VMCv20240226	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VVVDR5	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainImageID	Multiframe	VVVXDR1	UID of frame that this auxilliary image is related to	bigint	8		-99999999	obs.field
mainVarType	vvvVivaXMatchCatalogue	VVVDR5	The single variability type adopted by us to group the crossmatched sources {catalogue TType keyword: MainVarType}	varchar	16		NONE
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	SHARKSv20210222	the unique mapID	int	4			meta.id
mapID	CombinedFilters	SHARKSv20210421	the unique mapID	int	4			meta.id
mapID	CombinedFilters	ULTRAVISTADR4	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VHSv20201209	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VMCDEEPv20230713	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VMCDR5	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VMCv20191212	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VMCv20210708	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VMCv20230816	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VMCv20240226	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VVVDR5	the unique mapID	int	4			meta.id
mapID	CombinedFilters	VVVXDR1	the unique mapID	int	4			meta.id
mapID	MapApertureIDsultraVistaMapLc, MapApertureIDsultravistaDual, ultravistaRemeasMergeLog	ULTRAVISTADR4	UID of matched-aperture product	int	4			meta_id
mapID	MapApertureIDsvikingZY_selJ	VIKINGZYSELJv20170124	UID of matched-aperture product	int	4			meta_id
mapID	MapApertureIDsvikingZY_selJ, vikingZY_selJ_RemeasMergeLog	VIKINGZYSELJv20160909	UID of matched-aperture product	int	4			meta_id
mapID	MapFilterLupt	SHARKSv20210222	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	SHARKSv20210421	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	ULTRAVISTADR4	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VHSv20201209	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VMCDEEPv20230713	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VMCDR5	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VMCv20191212	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VMCv20210708	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VMCv20230816	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VMCv20240226	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VVVDR5	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFilterLupt	VVVXDR1	the unique mapID ID	int	4			meta.id;meta.main
mapID	MapFrameStatus	SHARKSv20210222	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	SHARKSv20210421	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	ULTRAVISTADR4	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VHSv20201209	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VMCDEEPv20230713	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VMCDR5	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VMCv20191212	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VMCv20210708	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VMCv20230816	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VMCv20240226	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VVVDR5	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapFrameStatus	VVVXDR1	UID of matched-aperture product	int	4		-99999999	meta_id_--N/_-99999999
mapID	MapSurveyTables	SHARKSv20210421	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	ULTRAVISTADR4	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VHSv20201209	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VMCDEEPv20230713	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VMCDR5	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VMCv20191212	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VMCv20210708	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VMCv20230816	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VMCv20240226	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VVVDR5	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables	VVVXDR1	the UID of the matched-aperture product	int	4			meta.id
mapID	MapSurveyTables, RequiredMapAverages	SHARKSv20210222	the UID of the matched-aperture product	int	4			meta.id
mapID	RequiredMatchedApertureProduct	SHARKSv20210222	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	SHARKSv20210421	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	ULTRAVISTADR4	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VHSv20201209	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VMCDEEPv20230713	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VMCDR5	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VMCv20191212	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VMCv20210708	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VMCv20230816	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VMCv20240226	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VVVDR5	the UID of the matched-aperture product	int	4			meta.main;meta.id
mapID	RequiredMatchedApertureProduct	VVVXDR1	the UID of the matched-aperture 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 matched-aperture product that this remeasurement is part of, see RequiredMatchedApertureProduct	int	4
mapID	ultravistaMapRemeasurement	ULTRAVISTADR4	UID of the matched-aperture product that this remeasurement is part of, see RequiredMatchedApertureProduct {catalogue extension keyword:  MAPID}	int	4
mapID	vikingMapRemeasAver	VIKINGZYSELJv20160909	UID of the matched-aperture product that this remeasurement is part of, see RequiredMatchedApertureProduct	int	4
mapID	vikingMapRemeasAver	VIKINGZYSELJv20170124	UID of the matched-aperture product that this remeasurement is part of, see RequiredMatchedApertureProduct	int	4
mapID	vikingMapRemeasurement	VIKINGZYSELJv20160909	UID of the matched-aperture product that this remeasurement is part of, see RequiredMatchedApertureProduct {catalogue extension keyword:  MAPID}	int	4
mapID	vikingMapRemeasurement	VIKINGZYSELJv20170124	UID of the matched-aperture 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	SHARKSv20210222	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	SHARKSv20210421	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	ULTRAVISTADR4	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VHSv20201209	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VMCDEEPv20230713	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VMCDR5	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VMCv20191212	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VMCv20210708	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VMCv20230816	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VMCv20240226	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VVVDR5	running number referring to the surveyID and tableID	int	4
mapTableID	MapSurveyTables	VVVXDR1	running number referring to the surveyID and tableID	int	4
mapType	RequiredMatchedApertureProduct	SHARKSv20210222	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	SHARKSv20210421	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	ULTRAVISTADR4	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VHSv20201209	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VMCDEEPv20230713	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VMCDR5	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VMCv20191212	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VMCv20210708	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VMCv20230816	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VMCv20240226	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VVVDR5	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
mapType	RequiredMatchedApertureProduct	VVVXDR1	type of matched-aperture product (SourceRemeasurement (0), Variability (1), TilePawPrint(2), Calibration (3)	smallint	2
maskID	Multiframe	SHARKSv20210222	UID of library object mask frame {image extension keyword: CIR_OPM}	bigint	8		-99999999	obs.field
maskID	Multiframe	SHARKSv20210421	UID of library object mask frame {image extension keyword: CIR_OPM}	bigint	8		-99999999	obs.field
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	UID of library object mask frame {image extension keyword: CIR_OPM}	bigint	8		-99999999	obs.field
maskID	Multiframe	VMCv20210708	UID of library object mask frame {image extension keyword: CIR_OPM}	bigint	8		-99999999	obs.field
maskID	Multiframe	VMCv20230816	UID of library object mask frame {image extension keyword: CIR_OPM}	bigint	8		-99999999	obs.field
maskID	Multiframe	VMCv20240226	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	VVVDR5	UID of library object mask frame {image extension keyword: CIR_OPM}	bigint	8		-99999999	obs.field
maskID	Multiframe	VVVXDR1	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	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	UID of library object mask frame	bigint	8		-99999999	obs.field
masktype	Multiframe	SHARKSv20210222	Mask type {image primary HDU keyword: MASKTYPE}	tinyint	1		0
masktype	Multiframe	SHARKSv20210421	Mask type {image primary HDU keyword: MASKTYPE}	tinyint	1		0
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	Mask type {image primary HDU keyword: MASKTYPE}	tinyint	1		0
masktype	Multiframe	VMCv20210708	Mask type {image primary HDU keyword: MASKTYPE}	tinyint	1		0
masktype	Multiframe	VMCv20230816	Mask type {image primary HDU keyword: MASKTYPE}	tinyint	1		0
masktype	Multiframe	VMCv20240226	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	VVVDR5	Mask type {image primary HDU keyword: MASKTYPE}	tinyint	1		0
masktype	Multiframe	VVVXDR1	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	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	Mask type	tinyint	1		0
masterObjID	sharksSourceNeighbours	SHARKSv20210421	The unique ID in sharksSource (=sourceID)	bigint	8			meta.id;meta.main
masterObjID	sharksSourceNeighbours, sharksSourceXDetection, sharksSourceXSSASource, sharksSourceXallwise_sc, sharksSourceXtwomass_psc, sharksSourceXtwompzPhotoz, sharksSourceXwiseScosPhotoz, sharksSourceXwise_allskysc	SHARKSv20210222	The unique ID in sharksSource (=sourceID)	bigint	8			meta.id;meta.main
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, vhsSourceXGDR1tgas_source, vhsSourceXGDR2gaia_source, vhsSourceXPawPrints, vhsSourceXSKYMAP_masterDR2	VHSv20201209	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, vikingSourceXDetection, vikingSourceXGDR1gaia_source, vikingSourceXGDR1tgas_source	VIKINGv20170715	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	VMCv20180702	The unique ID in vmcPsfSource (=psfSourceID)	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	VMCv20191212	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	VMCDR5	The unique ID in vmcPsfSource (=psfSourceID)	bigint	8			meta.id;meta.main
masterObjID	vmcPsfSourceXGDR1gaia_source, vmcPsfSourceXGDR2gaia_source, vmcPsfSourceXGEDR3gaia_source, vmcPsfSourceXSKYMAP_masterDR2, vmcPsfSourceXSMASHDR2_deep, vmcPsfSourceXSMASHDR2_object, vmcPsfSourceXSMASHDR2_source	VMCv20240226	The unique ID in vmcPsfSource (=psfSourceID)	bigint	8			meta.id;meta.main
masterObjID	vmcPsfSourceXGDR1gaia_source, vmcPsfSourceXSKYMAP_masterDR2, vmcPsfSourceXcatwise_2020	VMCv20210708	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	VMCv20160822	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	VMCv20170411	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	VMCv20191212	The unique ID in vmcSource (=sourceID)	bigint	8			meta.id;meta.main
masterObjID	vmcSourceNeighbours	VMCv20240226	The unique ID in vmcSource (=sourceID)	bigint	8			meta.id;meta.main
masterObjID	vmcSourceNeighbours, vmcSourceXGDR1gaia_source, vmcSourceXGDR1tgas_source, vmcSourceXGDR2gaia_source, vmcSourceXPsfSource, vmcSourceXogle3LpvLmcSource, vmcSourceXogle3LpvSmcSource, vmcSourceXogle4CepLmcSource, vmcSourceXogle4CepSmcSource, vmcSourceXogle4RRLyrLmcSource, vmcSourceXogle4RRLyrSmcSource, vmcSourceXravedr5Source, vmcSourceXtwompzPhotoz, vmcSourceXwiseScosPhotoz	VMCDR5	The unique ID in vmcSource (=sourceID)	bigint	8			meta.id;meta.main
masterObjID	vmcSourceNeighbours, vmcSourceXGEDR3gaia_source, vmcSourceXSMASHDR2_deep, vmcSourceXSMASHDR2_object, vmcSourceXSMASHDR2_source	VMCv20230816	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, vmcSourceXPsfDetections	VMCv20180702	The unique ID in vmcSource (=sourceID)	bigint	8			meta.id;meta.main
masterObjID	vmcSourceNeighbours, vmcSourceXSKYMAP_masterDR2, vmcSourceXcatwise_2020	VMCv20210708	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, vmcSourceXxmm3dr4	VMCv20140428	The unique ID in vmcSource (=sourceID)	bigint	8			meta.id;meta.main
masterObjID	vmcdeepSourceNeighbours, vmcdeepSourceXSSASource, vmcdeepSourceXSynopticSource, vmcdeepSourceXallwise_sc, vmcdeepSourceXtwomass_psc, vmcdeepSourceXwise_allskysc	VMCDEEPv20230713	The unique ID in vmcdeepSource (=sourceID)	bigint	8			meta.id;meta.main
masterObjID	vvvPsfDaophotJKsSourceXDR11gpsSource, vvvPsfDaophotJKsSourceXDR8gpsSource, vvvPsfDaophotJKsSourceXGDR1gaia_source, vvvPsfDaophotJKsSourceXGDR1tgas_source, vvvPsfDaophotJKsSourceXGDR2gaia_source, vvvPsfDaophotJKsSourceXGEDR3gaia_source, vvvPsfDaophotJKsSourceXPS1DR2_objectThin, vvvPsfDaophotJKsSourceXSSASource, vvvPsfDaophotJKsSourceXallwise_sc, vvvPsfDaophotJKsSourceXdecapsSource, vvvPsfDaophotJKsSourceXglimpse1_hrc, vvvPsfDaophotJKsSourceXglimpse1_mca, vvvPsfDaophotJKsSourceXglimpse2_hrc, vvvPsfDaophotJKsSourceXglimpse2_mca, vvvPsfDaophotJKsSourceXiras_psc, vvvPsfDaophotJKsSourceXtwomass_psc, vvvPsfDaophotJKsSourceXtwomass_xsc, vvvPsfDaophotJKsSourceXvphasDr3Source, vvvPsfDaophotJKsSourceXwise_allskysc, vvvPsfDaophotJKsSourceXxmm3dr4	VVVDR5	The unique ID in vvvPsfDaophotJKsSource (=psfID)	bigint	8			meta.id;meta.main
masterObjID	vvvPsfDophotZYJHKsSourceXDR11gpsSource, vvvPsfDophotZYJHKsSourceXDR8gpsSource, vvvPsfDophotZYJHKsSourceXGDR1gaia_source, vvvPsfDophotZYJHKsSourceXGDR1tgas_source, vvvPsfDophotZYJHKsSourceXGDR2gaia_source, vvvPsfDophotZYJHKsSourceXGEDR3gaia_source, vvvPsfDophotZYJHKsSourceXPS1DR2_objectThin, vvvPsfDophotZYJHKsSourceXSSASource, vvvPsfDophotZYJHKsSourceXallwise_sc, vvvPsfDophotZYJHKsSourceXdecapsSource, vvvPsfDophotZYJHKsSourceXglimpse1_hrc, vvvPsfDophotZYJHKsSourceXglimpse1_mca, vvvPsfDophotZYJHKsSourceXglimpse2_hrc, vvvPsfDophotZYJHKsSourceXglimpse2_mca, vvvPsfDophotZYJHKsSourceXiras_psc, vvvPsfDophotZYJHKsSourceXtwomass_psc, vvvPsfDophotZYJHKsSourceXtwomass_xsc, vvvPsfDophotZYJHKsSourceXvphasDr3Source, vvvPsfDophotZYJHKsSourceXwise_allskysc, vvvPsfDophotZYJHKsSourceXxmm3dr4	VVVDR5	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, vvvSourceXDR11gpsSource, vvvSourceXDR8gpsSource, vvvSourceXGDR1gaia_source, vvvSourceXGDR1tgas_source, vvvSourceXGDR2gaia_source, vvvSourceXGEDR3gaia_source, vvvSourceXPS1DR2_objectThin, vvvSourceXParallaxCatalogue, vvvSourceXProperMotionCatalogue, vvvSourceXPsfDaophotJKsSource, vvvSourceXPsfDophotZYJHKsSource, vvvSourceXVivaCatalogue, vvvSourceXallwise_sc, vvvSourceXdecapsSource, vvvSourceXglimpse1_hrc, vvvSourceXglimpse1_mca, vvvSourceXglimpse2_hrc, vvvSourceXglimpse2_mca, vvvSourceXvphasDr3Source, vvvSourceXwise_allskysc, vvvSourceXxmm3dr4	VVVDR5	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
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
match_p	gaiaxwise_matches	GAIAXWISE	Overall probability that the Gaia and WISE sources are detections of the same object, as given by equation 26 of Wilson & Naylor (2018a, MNRAS, 473, 5570).	float	8	dimensionless
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
matched_transits	gaia_source	GAIAEDR3	The number of transits matched to this source	smallint	2			meta.number
matched_transits_removed	gaia_source	GAIAEDR3	The number of transits removed from an existing source in the current cycle	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 N3-S7-S11 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 N3-S7-S11-L15 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 N3-S7 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	SHARKSv20210222	The maximum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
maxDec	CurrentAstrometry	SHARKSv20210421	The maximum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	The maximum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
maxDec	CurrentAstrometry	VMCv20210708	The maximum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
maxDec	CurrentAstrometry	VMCv20230816	The maximum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
maxDec	CurrentAstrometry	VMCv20240226	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	VVVDR5	The maximum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
maxDec	CurrentAstrometry	VVVXDR1	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	sharksCurrentAstrometry, 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 G-band time series	float	8	mag		phot.mag;stat.max
maxJitSize	Multiframe	SHARKSv20210222	SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT}	real	4	arcsec	-0.9999995e9
maxJitSize	Multiframe	SHARKSv20210421	SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT}	real	4	arcsec	-0.9999995e9
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT}	real	4	arcsec	-0.9999995e9
maxJitSize	Multiframe	VMCv20210708	SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT}	real	4	arcsec	-0.9999995e9
maxJitSize	Multiframe	VMCv20230816	SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT}	real	4	arcsec	-0.9999995e9
maxJitSize	Multiframe	VMCv20240226	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	VVVDR5	SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT}	real	4	arcsec	-0.9999995e9
maxJitSize	Multiframe	VVVXDR1	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	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	SADT maximum jitter size	real	4	arcsec	-0.9999995e9
maxMoonFli	Multiframe	SHARKSv20210222	Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI}	real	4		-0.9999995e9
maxMoonFli	Multiframe	SHARKSv20210421	Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI}	real	4		-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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI}	real	4		-0.9999995e9
maxMoonFli	Multiframe	VMCv20210708	Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI}	real	4		-0.9999995e9
maxMoonFli	Multiframe	VMCv20230816	Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI}	real	4		-0.9999995e9
maxMoonFli	Multiframe	VMCv20240226	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	VVVDR5	Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI}	real	4		-0.9999995e9
maxMoonFli	Multiframe	VVVXDR1	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	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	Requested maximum fractional lunar illumination	real	4		-0.9999995e9
maxPllx	sharksVariability	SHARKSv20210222	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	sharksVariability	SHARKSv20210421	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	vmcVariability	VMCDR5	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	vmcVariability	VMCv20191212	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	vmcVariability	VMCv20210708	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	vmcVariability	VMCv20230816	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	vmcVariability	VMCv20240226	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	vmcdeepVariability	VMCDEEPv20230713	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxPllx	vvvVariability	VVVDR5	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
maxRa	CurrentAstrometry	SHARKSv20210222	The maximum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
maxRa	CurrentAstrometry	SHARKSv20210421	The maximum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	The maximum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
maxRa	CurrentAstrometry	VMCv20210708	The maximum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
maxRa	CurrentAstrometry	VMCv20230816	The maximum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
maxRa	CurrentAstrometry	VMCv20240226	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	VVVDR5	The maximum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
maxRa	CurrentAstrometry	VVVXDR1	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	sharksCurrentAstrometry, 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	multi-colour 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
mdetID	catwise_2020, catwise_prelim	WISE	source ID in mdet list	int	4
mean	phot_variable_time_series_g_fov_statistical_parameters	GAIADR1	Mean magnitude of the G-band time series	float	8	mag		phot.mag;stat.mean
mean_epoch	masterDR2	SKYMAPPER	Mean MJD epoch of the observations	float	8	d		time.epoch;stat.mean
mean_obs_time	phot_variable_time_series_g_fov_statistical_parameters	GAIADR1	Mean observation time (with respect to T0) of G-band time series	float	8	days		time.epoch;stat.mean
mean_varpi_factor_al	gaia_source	GAIADR2	Mean parallax factor Along-Scan	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	VMCDR5	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	vmcSynopticMergeLog	VMCv20191212	Mean modified julian date of frameset.	float	8	days	-0.9999995e9	time.epoch
meanMjdObs	vmcSynopticMergeLog	VMCv20210708	Mean modified julian date of frameset.	float	8	days	-0.9999995e9	time.epoch
meanMjdObs	vmcSynopticMergeLog	VMCv20230816	Mean modified julian date of frameset.	float	8	days	-0.9999995e9	time.epoch
meanMjdObs	vmcSynopticMergeLog	VMCv20240226	Mean modified julian date of frameset.	float	8	days	-0.9999995e9	time.epoch
meanMjdObs	vmcdeepSynopticMergeLog	VMCDEEPv20230713	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
MeanObsMJD	catwise_2020, catwise_prelim	WISE	mean observation epoch	float	8	MJD
median	phot_variable_time_series_g_fov_statistical_parameters	GAIADR1	Median magnitude of the G-band 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 G-band time series values	float	8	mag		phot.mag;stat.value
medPa	MultiframeDetector	SHARKSv20210222	[deg] Mean PA from N to E {catalogue extension keyword:  MED_PA}	real	4		-0.9999995e9
medPa	MultiframeDetector	SHARKSv20210421	[deg] Mean PA from N to E {catalogue extension keyword:  MED_PA}	real	4		-0.9999995e9
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	VHSv20201209	[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	VMCDEEPv20230713	[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	VMCDR5	[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	VMCv20191212	[deg] Mean PA from N to E {catalogue extension keyword:  MED_PA}	real	4		-0.9999995e9
medPa	MultiframeDetector	VMCv20210708	[deg] Mean PA from N to E {catalogue extension keyword:  MED_PA}	real	4		-0.9999995e9
medPa	MultiframeDetector	VMCv20230816	[deg] Mean PA from N to E {catalogue extension keyword:  MED_PA}	real	4		-0.9999995e9
medPa	MultiframeDetector	VMCv20240226	[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	VVVDR5	[deg] Mean PA from N to E {catalogue extension keyword:  MED_PA}	real	4		-0.9999995e9
medPa	MultiframeDetector	VVVXDR1	[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	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	[deg] Mean PA from N to E	real	4		-0.9999995e9
mergedClass	sharksSource	SHARKSv20210222	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	sharksSource	SHARKSv20210421	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	ultravistaSource	ULTRAVISTADR4	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VHSv20201209	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VMCDR5	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VMCv20191212	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VMCv20210708	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VMCv20230816	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VMCv20240226	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VVVDR2	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	VVVDR5	Class flag from available measurements (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 (1|0|-1|-2|-3|-9=galaxy|noise|stellar|probableStar|probableGalaxy|saturated)	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, self-consistent probability values for a given classification code: Flag Meaning Probability (%) Star Galaxy Noise Saturated -9 Saturated 0.0 0.0 5.0 95.0 -3 Probable galaxy 25.0 70.0 5.0 0.0 -2 Probable star 70.0 25.0 5.0 0.0 -1 Star 90.0 5.0 5.0 0.0 0 Noise 5.0 5.0 90.0 0.0 +1 Galaxy 5.0 90.0 5.0 0.0 Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent: P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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	sharksSource	SHARKSv20210222	Merged N(0,1) stellarness-of-profile 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 N1/2 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	sharksSource	SHARKSv20210421	Merged N(0,1) stellarness-of-profile 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 N1/2 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	ultravistaSource	ULTRAVISTADR4	Merged S-Extractor classification statistic	real	4		-0.9999995e9	stat
			Inverse variance-weighted mean of the available individual passband S-Extractor classification statistics *ClassStat.
mergedClassStat	ultravistaSourceRemeasurement	ULTRAVISTADR4	Merged N(0,1) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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	VHSv20201209	Merged N(0,1) stellarness-of-profile 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 N1/2 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 S-Extractor classification statistic	real	4		-0.9999995e9	stat
			Inverse variance-weighted mean of the available individual passband S-Extractor classification statistics *ClassStat.
mergedClassStat	videoSource	VIDEODR3	Merged S-Extractor classification statistic	real	4		-0.9999995e9	stat
			Inverse variance-weighted mean of the available individual passband S-Extractor classification statistics *ClassStat.
mergedClassStat	videoSource	VIDEODR4	Merged S-Extractor classification statistic	real	4		-0.9999995e9	stat
			Inverse variance-weighted mean of the available individual passband S-Extractor classification statistics *ClassStat.
mergedClassStat	videoSource	VIDEODR5	Merged S-Extractor classification statistic	real	4		-0.9999995e9	stat
			Inverse variance-weighted mean of the available individual passband S-Extractor classification statistics *ClassStat.
mergedClassStat	videoSource	VIDEOv20100513	Merged S-Extractor classification statistic	real	4		-0.9999995e9	stat
			Inverse variance-weighted mean of the available individual passband S-Extractor classification statistics *ClassStat.
mergedClassStat	videoSource	VIDEOv20111208	Merged S-Extractor classification statistic	real	4		-0.9999995e9	stat
			Inverse variance-weighted mean of the available individual passband S-Extractor classification statistics *ClassStat.
mergedClassStat	vikingSource	VIKINGDR2	Merged N(0,1) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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	VMCDR5	Merged N(0,1) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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	VMCv20191212	Merged N(0,1) stellarness-of-profile 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 N1/2 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	VMCv20210708	Merged N(0,1) stellarness-of-profile 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 N1/2 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	VMCv20230816	Merged N(0,1) stellarness-of-profile 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 N1/2 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	VMCv20240226	Merged N(0,1) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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	vmcdeepSource, vmcdeepSynopticSource	VMCDEEPv20230713	Merged N(0,1) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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	VVVDR5	Merged N(0,1) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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) stellarness-of-profile 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 N1/2 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
mergeLogTable	Programme	SHARKSv20210222	Table name of curation log for source merging	varchar	64			??
mergeLogTable	Programme	SHARKSv20210421	Table name of curation log for source merging	varchar	64			??
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	Table name of curation log for source merging	varchar	64			??
mergeLogTable	Programme	VMCv20210708	Table name of curation log for source merging	varchar	64			??
mergeLogTable	Programme	VMCv20230816	Table name of curation log for source merging	varchar	64			??
mergeLogTable	Programme	VMCv20240226	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	VVVDR5	Table name of curation log for source merging	varchar	64			??
mergeLogTable	Programme	VVVXDR1	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	sharksMergeLog	SHARKSv20210421	version number of the software used to merge the frames	real	4			meta.id;meta.software
mergeSwVersion	sharksMergeLog, sharksTileSet	SHARKSv20210222	version number of the software used to merge the frames	real	4			meta.id;meta.software
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	VHSv20201209	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, 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	VMCDR5	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	VMCv20191212	version number of the software used to merge the frames	real	4			meta.id;meta.software
mergeSwVersion	vmcMergeLog	VMCv20210708	version number of the software used to merge the frames	real	4			meta.id;meta.software
mergeSwVersion	vmcMergeLog	VMCv20230816	version number of the software used to merge the frames	real	4			meta.id;meta.software
mergeSwVersion	vmcMergeLog	VMCv20240226	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	vmcdeepMergeLog, vmcdeepSynopticMergeLog, vmcdeepTileSet	VMCDEEPv20230713	version number of the software used to merge the frames	real	4			meta.id;meta.software
mergeSwVersion	vvvMergeLog	VVVDR2	version number of the software used to merge the frames	real	4			meta.id;meta.software
mergeSwVersion	vvvMergeLog	VVVDR5	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	SHARKSv20210222	CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky)	varchar	64			??
method	RequiredDiffImage	SHARKSv20210421	CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky)	varchar	64			??
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky)	varchar	64			??
method	RequiredDiffImage	VMCv20210708	CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky)	varchar	64			??
method	RequiredDiffImage	VMCv20230816	CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky)	varchar	64			??
method	RequiredDiffImage	VMCv20240226	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	VVVDR5	CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky)	varchar	64			??
method	RequiredDiffImage	VVVXDR1	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 SAGE-SMC_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 SAGE-SMC_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 SAGE-SMC_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 SAGE-SMC_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 B-band K-correction	real	4		0.000
MGC_BEST_Z	mgcGalaxyStruct	MGC	Best redshift	real	4		9.99999
MGC_BEST_ZQUAL	mgcGalaxyStruct	MGC	Quality of best redshift (0-2 = BAD, 3-5=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	SHARKSv20210222	The minimum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
minDec	CurrentAstrometry	SHARKSv20210421	The minimum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	The minimum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
minDec	CurrentAstrometry	VMCv20210708	The minimum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
minDec	CurrentAstrometry	VMCv20230816	The minimum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
minDec	CurrentAstrometry	VMCv20240226	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	VVVDR5	The minimum Dec (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.dec;meta.main
minDec	CurrentAstrometry	VVVXDR1	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	sharksCurrentAstrometry, 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	SHARKSv20210222	Minimum size for images (pixels) {catalogue extension keyword:  MINPIX}	tinyint	1		0
minImageSize	MultiframeDetector	SHARKSv20210421	Minimum size for images (pixels) {catalogue extension keyword:  MINPIX}	tinyint	1		0
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	Minimum size for images (pixels) {catalogue extension keyword:  MINPIX}	tinyint	1		0
minImageSize	MultiframeDetector	VMCv20210708	Minimum size for images (pixels) {catalogue extension keyword:  MINPIX}	tinyint	1		0
minImageSize	MultiframeDetector	VMCv20230816	Minimum size for images (pixels) {catalogue extension keyword:  MINPIX}	tinyint	1		0
minImageSize	MultiframeDetector	VMCv20240226	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	VVVDR5	Minimum size for images (pixels) {catalogue extension keyword:  MINPIX}	tinyint	1		0
minImageSize	MultiframeDetector	VVVXDR1	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	sharksMultiframeDetector, 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 G-band time series	float	8	mag		phot.mag;stat.min
minMoonDist	Multiframe	SHARKSv20210222	Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST}	real	4	deg	-0.9999995e9
minMoonDist	Multiframe	SHARKSv20210421	Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST}	real	4	deg	-0.9999995e9
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST}	real	4	deg	-0.9999995e9
minMoonDist	Multiframe	VMCv20210708	Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST}	real	4	deg	-0.9999995e9
minMoonDist	Multiframe	VMCv20230816	Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST}	real	4	deg	-0.9999995e9
minMoonDist	Multiframe	VMCv20240226	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	VVVDR5	Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST}	real	4	deg	-0.9999995e9
minMoonDist	Multiframe	VVVXDR1	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	sharksMultiframe, 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	sharksVariability	SHARKSv20210222	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	sharksVariability	SHARKSv20210421	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	vmcVariability	VMCDR5	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	vmcVariability	VMCv20191212	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	vmcVariability	VMCv20210708	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	vmcVariability	VMCv20230816	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	vmcVariability	VMCv20240226	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	vmcdeepVariability	VMCDEEPv20230713	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minPllx	vvvVariability	VVVDR5	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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
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 z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
minRa	CurrentAstrometry	SHARKSv20210222	The minimum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
minRa	CurrentAstrometry	SHARKSv20210421	The minimum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	The minimum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
minRa	CurrentAstrometry	VMCv20210708	The minimum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
minRa	CurrentAstrometry	VMCv20230816	The minimum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
minRa	CurrentAstrometry	VMCv20240226	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	VVVDR5	The minimum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
minRa	CurrentAstrometry	VVVXDR1	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	sharksCurrentAstrometry, ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry	VSAQC	The minimum RA (J2000) on the device	float	8	Degrees	-0.9999995e9	pos.eq.ra
mjd	sharksDetection	SHARKSv20210222	The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff}	float	8	day		time.epoch
mjd	sharksDetection	SHARKSv20210421	The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff}	float	8	day		time.epoch
mjd	smashdr2_source	SMASH	Exposure Modified Julian Date	float	8
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	vhsDetection	VHSv20201209	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	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	VMCDR5	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	vmcDetection	VMCv20191212	The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff}	float	8	day		time.epoch
mjd	vmcDetection	VMCv20210708	The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff}	float	8	day		time.epoch
mjd	vmcDetection	VMCv20230816	The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff}	float	8	day		time.epoch
mjd	vmcDetection	VMCv20240226	The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff}	float	8	day		time.epoch
mjd	vmcdeepDetection	VMCDEEPv20230713	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, vvvDetectionPawPrints, vvvDetectionTiles	VVVDR5	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	SHARKSv20210222	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	SHARKSv20210421	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	ULTRAVISTADR4	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSDR1	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSDR2	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSDR3	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSDR4	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSDR5	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSDR6	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20120926	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20130417	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20140409	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20150108	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20160114	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20160507	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20170630	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20180419	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VHSv20201209	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIDEODR2	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIDEODR3	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIDEODR4	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIDEODR5	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIDEOv20111208	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGDR2	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGDR3	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGDR4	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20110714	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20111019	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20130417	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20140402	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20150421	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20151230	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20160406	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20161202	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VIKINGv20170715	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCDEEPv20230713	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCDR1	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCDR2	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCDR3	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCDR4	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCDR5	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20110816	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20110909	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20120126	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20121128	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20130304	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20130805	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20140428	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20140903	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20150309	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20151218	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20160311	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20160822	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20170109	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20170411	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20171101	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20180702	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20181120	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20191212	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20210708	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20230816	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VMCv20240226	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VVVDR1	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VVVDR2	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VVVDR5	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VVVXDR1	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	Multiframe	VVVv20110718	Modified Julian Date of the observation end {image primary HDU keyword: MJD-END}	float	8		-0.9999995e9	time.epoch
mjdEnd	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	Modified Julian Date of the observation end	float	8		-0.9999995e9	time.epoch
mjdMean	MultiframeDetector	SHARKSv20210222	Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD}	float	8	days	-0.9999995e9
mjdMean	MultiframeDetector	SHARKSv20210421	Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD}	float	8	days	-0.9999995e9
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD}	float	8	days	-0.9999995e9
mjdMean	MultiframeDetector	VMCv20210708	Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD}	float	8	days	-0.9999995e9
mjdMean	MultiframeDetector	VMCv20230816	Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD}	float	8	days	-0.9999995e9
mjdMean	MultiframeDetector	VMCv20240226	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	VVVDR5	Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD}	float	8	days	-0.9999995e9
mjdMean	MultiframeDetector	VVVXDR1	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	sharksMultiframeDetector, ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector	VSAQC	Mean MJD of all images comprising this image	float	8	days	-0.9999995e9
mjdObs	Multiframe	SHARKSv20210222	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	SHARKSv20210421	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	ULTRAVISTADR4	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSDR1	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSDR2	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSDR3	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSDR4	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSDR5	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSDR6	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20120926	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20130417	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20140409	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20150108	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20160114	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20160507	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20170630	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20180419	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VHSv20201209	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIDEODR2	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIDEODR3	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIDEODR4	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIDEODR5	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIDEOv20100513	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIDEOv20111208	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGDR2	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGDR3	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGDR4	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20110714	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20111019	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20130417	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20140402	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20150421	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20151230	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20160406	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20161202	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VIKINGv20170715	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCDEEPv20230713	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCDR1	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCDR2	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCDR3	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCDR4	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCDR5	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20110816	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20110909	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20120126	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20121128	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20130304	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20130805	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20140428	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20140903	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20150309	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20151218	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20160311	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20160822	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20170109	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20170411	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20171101	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20180702	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20181120	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20191212	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20210708	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20230816	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VMCv20240226	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VVVDR1	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VVVDR2	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VVVDR5	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VVVXDR1	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VVVv20100531	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	Multiframe	VVVv20110718	Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS}	float	8		-0.9999995e9	time.epoch
mjdObs	sharksMultiframe, 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 (MM-DD-YYYY)	datetime	8	mm-dd-yyyy		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	sharksSourceXDetectionBestMatch	SHARKSv20210222	separation from expected position given astrometric model in sharksSource variability.	real	4	arcsec	-0.9999995e9	pos.posAng
modelDistSecs	sharksSourceXDetectionBestMatch	SHARKSv20210421	separation from expected position given astrometric model in sharksSource variability.	real	4	arcsec	-0.9999995e9	pos.posAng
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	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	VMCDR5	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	vmcSourceXSynopticSourceBestMatch	VMCv20191212	separation from expected position given astrometric model in vmcSource variability.	real	4	arcsec	-0.9999995e9	pos.posAng
modelDistSecs	vmcSourceXSynopticSourceBestMatch	VMCv20210708	separation from expected position given astrometric model in vmcSource variability.	real	4	arcsec	-0.9999995e9	pos.posAng
modelDistSecs	vmcSourceXSynopticSourceBestMatch	VMCv20230816	separation from expected position given astrometric model in vmcSource variability.	real	4	arcsec	-0.9999995e9	pos.posAng
modelDistSecs	vmcSourceXSynopticSourceBestMatch	VMCv20240226	separation from expected position given astrometric model in vmcSource variability.	real	4	arcsec	-0.9999995e9	pos.posAng
modelDistSecs	vmcdeepSourceXSynopticSourceBestMatch	VMCDEEPv20230713	separation from expected position given astrometric model in vmcdeepSource 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	VVVDR5	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
momentM3C	Detection	PS1DR2	Cosine of trefoil second moment term: r^2 cos(3 theta) = M_xxx - 3 * M_xyy.	real	4	arcsec^2	-999
momentM3S	Detection	PS1DR2	Sine of trefoil second moment: r^2 sin (3 theta) = 3 * M_xxy - M_yyy.	real	4	arcsec^2	-999
momentM4C	Detection	PS1DR2	Cosine of quadrupole second moment: r^2 cos (4 theta) = M_xxxx - 6 * M_xxyy + M_yyyy.	real	4	arcsec^2	-999
momentM4S	Detection	PS1DR2	Sine of quadrupole second moment: r^2 sin (4 theta) = 4 * M_xxxy - 4 * M_xyyy.	real	4	arcsec^2	-999
momentR1	Detection	PS1DR2	First radial moment.	real	4	arcsec	-999
momentRH	Detection	PS1DR2	Half radial moment (r^0.5 weighting).	real	4	arcsec^0.5	-999
momentXX	Detection	PS1DR2	Second moment M_xx.	real	4	arcsec^2	-999
momentXY	Detection	PS1DR2	Second moment M_xy.	real	4	arcsec^2	-999
momentYY	Detection	PS1DR2	Second moment M_yy.	real	4	arcsec^2	-999
moon_lev	allwise_sc	WISE	Scattered moonlight contamination flag. This is a four-character string, one character per band, in which the value is an integer indicates the fraction of single-exposure 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
moon_lev	wise_allskysc	WISE	Scattered moonlight contamination flag. This is a four-character string, one character per band, in which the value is an integer indicates the fraction of single-exposure 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.	char	4
MORPH_TYPE	mgcGalaxyStruct	MGC	SPD's EYEBALL morphology (1=E/S0, 2=Sabc, 3=Sd/Irr, 4=dE)	tinyint	1		0
morphClassFlag	MultiframeDetector	SHARKSv20210222	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	SHARKSv20210421	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	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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	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	VMCv20210708	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	VMCv20230816	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	VMCv20240226	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	VVVDR5	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	VVVXDR1	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	sharksMultiframeDetector, 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	SHARKSv20210222	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	SHARKSv20210421	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	ULTRAVISTADR4	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VHSv20201209	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VMCDEEPv20230713	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VMCDR5	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VMCv20191212	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VMCv20210708	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VMCv20230816	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VMCv20240226	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VVVDR5	Mosaicing software (typically SWARP)	varchar	16
mosaicSoft	RequiredMosaicTopLevel	VVVXDR1	Mosaicing software (typically SWARP)	varchar	16
mosaicTool	Programme	SHARKSv20210222	Name of mosaicing tool to be used	varchar	8		NONE	??
mosaicTool	Programme	SHARKSv20210421	Name of mosaicing tool to be used	varchar	8		NONE	??
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	VHSv20201209	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	VMCDEEPv20230713	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	VMCDR5	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	VMCv20191212	Name of mosaicing tool to be used	varchar	8		NONE	??
mosaicTool	Programme	VMCv20210708	Name of mosaicing tool to be used	varchar	8		NONE	??
mosaicTool	Programme	VMCv20230816	Name of mosaicing tool to be used	varchar	8		NONE	??
mosaicTool	Programme	VMCv20240226	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	VVVDR5	Name of mosaicing tool to be used	varchar	8		NONE	??
mosaicTool	Programme	VVVXDR1	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	sharksVarFrameSetInfo	SHARKSv20210222	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	sharksVarFrameSetInfo	SHARKSv20210421	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	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 be stars with small values of proper motion and parallax.
motionModel	vmcVarFrameSetInfo	VMCDR1	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	vmcVarFrameSetInfo	VMCDR2	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	vmcVarFrameSetInfo	VMCDR3	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	vmcVarFrameSetInfo	VMCDR4	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	vmcVarFrameSetInfo	VMCDR5	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	vmcVarFrameSetInfo	VMCv20110816	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	vmcVarFrameSetInfo	VMCv20110909	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	vmcVarFrameSetInfo	VMCv20120126	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	vmcVarFrameSetInfo	VMCv20121128	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	vmcVarFrameSetInfo	VMCv20130304	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	vmcVarFrameSetInfo	VMCv20130805	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	vmcVarFrameSetInfo	VMCv20140428	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	vmcVarFrameSetInfo	VMCv20140903	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	vmcVarFrameSetInfo	VMCv20150309	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	vmcVarFrameSetInfo	VMCv20151218	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	vmcVarFrameSetInfo	VMCv20160311	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	vmcVarFrameSetInfo	VMCv20160822	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	vmcVarFrameSetInfo	VMCv20170109	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	vmcVarFrameSetInfo	VMCv20170411	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	vmcVarFrameSetInfo	VMCv20171101	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	vmcVarFrameSetInfo	VMCv20180702	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	vmcVarFrameSetInfo	VMCv20181120	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	vmcVarFrameSetInfo	VMCv20191212	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	vmcVarFrameSetInfo	VMCv20210708	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	vmcVarFrameSetInfo	VMCv20230816	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	vmcVarFrameSetInfo	VMCv20240226	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	vmcdeepVarFrameSetInfo	VMCDEEPv20230713	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	vvvVarFrameSetInfo	VVVDR1	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	vvvVarFrameSetInfo	VVVDR2	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	vvvVarFrameSetInfo	VVVDR5	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	vvvVarFrameSetInfo	VVVv20100531	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	vvvVarFrameSetInfo	VVVv20110718	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.
mp_flg	twomass_psc	TWOMASS	Minor Planet Flag.	smallint	2			meta.code
mp_flg	twomass_sixx2_psc	TWOMASS	src is positionally associated with an asteroid, comet, etc	smallint	2
mp_key	twomass_xsc	TWOMASS	key to minor planet prediction DB record.	int	4			meta.id
MU_EFF	mgcBrightSpec	MGC	Effective surface brightness	real	4	mag arcsec^-2
mu_max	combo17CDFSSource	COMBO17	central surface brightness in Rmag	real	4	mag
muDec	sharksVariability	SHARKSv20210222	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	sharksVariability	SHARKSv20210421	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	ultravistaVariability	ULTRAVISTADR4	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	videoVariability	VIDEODR2	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	videoVariability	VIDEODR3	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	videoVariability	VIDEODR4	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	videoVariability	VIDEODR5	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	videoVariability	VIDEOv20100513	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	videoVariability	VIDEOv20111208	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGDR2	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGDR3	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGDR4	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20110714	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20111019	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20130417	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20140402	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20150421	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20151230	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20160406	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20161202	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vikingVariability	VIKINGv20170715	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCDR1	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCDR2	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCDR3	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCDR4	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCDR5	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20110816	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20110909	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20120126	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20121128	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20130304	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20130805	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20140428	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20140903	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20150309	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20151218	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20160311	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20160822	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20170109	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20170411	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20171101	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20180702	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20181120	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20191212	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20210708	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20230816	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcVariability	VMCv20240226	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vmcdeepVariability	VMCDEEPv20230713	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vvvVariability	VVVDR1	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vvvVariability	VVVDR2	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vvvVariability	VVVDR5	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.eq.dec;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vvvVariability	VVVv20100531	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muDec	vvvVariability	VVVv20110718	Proper motion in Dec	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.dec
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
MUK20FE	twomass	SIXDF	average surface brightness within the mu_K = 20mag/sq arcsec elliptical isophote (derived from K_M_K20FE, RADIUS and A_B)	real	4
multiframeID	CurrentAstrometry	SHARKSv20210421	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	ULTRAVISTADR4	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSDR1	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSDR2	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSDR3	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSDR4	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSDR5	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSDR6	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20120926	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20130417	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20140409	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20150108	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20160114	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20160507	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20170630	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20180419	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VHSv20201209	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIDEODR2	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIDEODR3	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIDEODR4	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIDEODR5	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIDEOv20100513	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIDEOv20111208	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGDR2	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGDR3	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGDR4	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20110714	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20111019	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20130417	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20140402	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20150421	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20151230	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20160406	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20161202	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VIKINGv20170715	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCDEEPv20230713	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCDR1	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCDR2	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCDR3	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCDR4	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCDR5	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20110816	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20110909	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20120126	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20121128	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20130304	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20130805	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20140428	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20140903	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20150309	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20151218	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20160311	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20160822	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20170109	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20170411	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20171101	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20180702	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20181120	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20191212	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20210708	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20230816	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VMCv20240226	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VVVDR1	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VVVDR2	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VVVDR5	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VVVXDR1	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VVVv20100531	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry	VVVv20110718	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	CurrentAstrometry, MapFrameStatus, MultiframeDetector, MultiframeDetectorEsoKeys, MultiframeEsoKeys	SHARKSv20210222	the UID of the relevant multiframe	bigint	8		-99999999	obs.field
multiframeID	EpochFrameStatus	SHARKSv20210421	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	ULTRAVISTADR4	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VHSDR5	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VHSDR6	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VHSv20160114	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VHSv20160507	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VHSv20170630	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VHSv20180419	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VHSv20201209	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VIKINGv20151230	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VIKINGv20160406	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VIKINGv20161202	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VIKINGv20170715	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCDEEPv20230713	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCDR4	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCDR5	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20151218	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20160311	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20160822	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20170109	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20170411	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20171101	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20180702	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20181120	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20191212	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20210708	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20230816	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VMCv20240226	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VVVDR5	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus	VVVXDR1	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	EpochFrameStatus, PreviousMFDZP, ProgrammeFrame	SHARKSv20210222	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	Multiframe	SHARKSv20210421	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	ULTRAVISTADR4	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSDR1	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSDR2	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSDR3	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSDR4	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSDR5	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSDR6	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20120926	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20130417	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20140409	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20150108	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20160114	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20160507	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20170630	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20180419	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VHSv20201209	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIDEODR2	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIDEODR3	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIDEODR4	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIDEODR5	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIDEOv20100513	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIDEOv20111208	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGDR2	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGDR3	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGDR4	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20110714	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20111019	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20130417	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20140402	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20150421	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20151230	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20160406	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20161202	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VIKINGv20170715	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCDEEPv20230713	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCDR1	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCDR2	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCDR3	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCDR4	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCDR5	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20110816	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20110909	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20120126	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20121128	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20130304	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20130805	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20140428	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20140903	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20150309	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20151218	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20160311	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20160822	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20170109	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20170411	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20171101	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20180702	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20181120	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20191212	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20210708	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20230816	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VMCv20240226	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VVVDR1	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VVVDR2	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VVVDR5	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VVVXDR1	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VVVv20100531	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe	VVVv20110718	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Multiframe, ProblemFrames	SHARKSv20210222	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8		-99999999	obs.field
multiframeID	Provenance	SHARKSv20210222	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	SHARKSv20210421	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	ULTRAVISTADR4	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSDR1	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSDR2	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSDR3	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSDR4	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSDR5	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSDR6	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20120926	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20130417	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20140409	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20150108	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20160114	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20160507	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20170630	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20180419	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VHSv20201209	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIDEODR2	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIDEODR3	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIDEODR4	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIDEODR5	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIDEOv20100513	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIDEOv20111208	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGDR2	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGDR3	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGDR4	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20110714	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20111019	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20130417	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20140402	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20150421	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20151230	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20160406	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20161202	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VIKINGv20170715	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCDEEPv20230713	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCDR1	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCDR2	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCDR3	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCDR4	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCDR5	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20110816	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20110909	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20120126	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20121128	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20130304	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20130805	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20140428	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20140903	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20150309	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20151218	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20160311	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20160822	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20170109	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20170411	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20171101	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20180702	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20181120	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20191212	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20210708	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20230816	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VMCv20240226	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VSAQC	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VVVDR1	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VVVDR2	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VVVDR5	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VVVXDR1	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VVVv20100531	the UID of the component frame	bigint	8			obs.field
multiframeID	Provenance	VVVv20110718	the UID of the component frame	bigint	8			obs.field
multiframeID	sharksAstrometricInfo	SHARKSv20210421	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
multiframeID	sharksAstrometricInfo, sharksDetection, sharksSourceXDetectionBestMatch	SHARKSv20210222	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
multiframeID	sharksCurrentAstrometry, sharksMultiframeDetector, ultravistaCurrentAstrometry, ultravistaMultiframeDetector, vhsCurrentAstrometry, vhsMultiframeDetector, videoCurrentAstrometry, videoMultiframeDetector, vikingCurrentAstrometry, vikingMultiframeDetector, vmcCurrentAstrometry, vmcMultiframeDetector, vvvCurrentAstrometry, vvvMultiframeDetector	VSAQC	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	sharksMultiframe, ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe	VSAQC	UID of the multiframe (assigned sequentially by the archive ingest process)	bigint	8			obs.field
multiframeID	ultravistaAstrometricInfo, ultravistaDetection, ultravistaSourceXDetectionBestMatch	ULTRAVISTADR4	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
multiframeID	ultravistaMapRemeasurement	ULTRAVISTADR4	the UID of the relevant multiframe {catalogue extension keyword:  VSA_MFID}	bigint	8			meta.id;obs.field
multiframeID	vhsAstrometricInfo	VHSDR6	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
multiframeID	vhsAstrometricInfo	VHSv20170630	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
multiframeID	vhsAstrometricInfo	VHSv20180419	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
multiframeID	vhsAstrometricInfo	VHSv20201209	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
multiframeID	vikingMapRemeasAver	VIKINGZYSELJv20160909	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	vikingMapRemeasAver	VIKINGZYSELJv20170124	the UID of the relevant multiframe	bigint	8			obs.field
multiframeID	vikingMapRemeasurement	VIKINGZYSELJv20160909	the UID of the relevant multiframe {catalogue extension keyword:  VSA_MFID}	bigint	8			obs.field
multiframeID	vikingMapRemeasurement	VIKINGZYSELJv20170124	the UID of the relevant multiframe {catalogue extension keyword:  VSA_MFID}	bigint	8			obs.field
multiframeID	vmcdeepAstrometricInfo, vmcdeepDetection	VMCDEEPv20230713	the UID of the relevant multiframe	bigint	8			meta.id;obs.field
muRa	sharksVariability	SHARKSv20210222	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	sharksVariability	SHARKSv20210421	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	ultravistaVariability	ULTRAVISTADR4	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	videoVariability	VIDEODR2	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	videoVariability	VIDEODR3	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	videoVariability	VIDEODR4	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	videoVariability	VIDEODR5	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	videoVariability	VIDEOv20100513	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	videoVariability	VIDEOv20111208	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGDR2	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGDR3	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGDR4	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20110714	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20111019	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20130417	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20140402	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20150421	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20151230	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20160406	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20161202	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vikingVariability	VIKINGv20170715	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCDR1	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCDR2	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCDR3	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCDR4	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCDR5	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20110816	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20110909	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20120126	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20121128	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20130304	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20130805	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20140428	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20140903	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20150309	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20151218	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20160311	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20160822	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20170109	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20170411	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20171101	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20180702	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20181120	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20191212	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20210708	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20230816	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcVariability	VMCv20240226	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vmcdeepVariability	VMCDEEPv20230713	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vvvVariability	VVVDR1	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vvvVariability	VVVDR2	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vvvVariability	VVVDR5	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.eq.ra;pos.pm
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vvvVariability	VVVv20100531	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
muRa	vvvVariability	VVVv20110718	Proper motion in RA	real	4	mas/yr	-0.9999995e9	pos.pm;pos.eq.ra
			The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.