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Glossary of VSA attributes

This Glossary alphabetically lists all attributes used in the VSAv20181120 database(s) held in the VSA. If you would like to have more information about the schema tables please use the VSAv20181120 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

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified 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 vvvParallaxCatalogue, vvvProperMotionCatalogue VVVDR4 Median of Ks band aperMag2 measurements from all epochs in the pawprint set {catalogue TType keyword: mag} real 4   -999999500.0  
mag1 glimpse_hrc_inter, glimpse_mca_inter GLIMPSE Magnitude in IRAC band 1 real 4 mag 99.999  
mag160 sage_lmcMips160Source SPITZER 160um magnitude float 8 mag    
mag1_err glimpse_hrc_inter, glimpse_mca_inter GLIMPSE 1sigma mag error (IRAC band 1) real 4 mag 99.999  
mag2 glimpse_hrc_inter, glimpse_mca_inter GLIMPSE Magnitude in IRAC band 2 real 4 mag 99.999  
mag24 sage_lmcMips24Source SPITZER 24um magnitude float 8 mag    
mag2_err glimpse_hrc_inter, glimpse_mca_inter GLIMPSE 1sigma mag error (IRAC band 2) real 4 mag 99.999  
mag3 glimpse_hrc_inter, glimpse_mca_inter GLIMPSE Magnitude in IRAC band 3 real 4 mag 99.999  
mag3_6 glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 3.6um IRAC (Band 1) magnitude real 4 mag 99.999  
mag3_6 sage_lmcIracSource SPITZER 3.6um magnitude real 4 mag    
mag3_6 sage_smcIRACv1_5Source SPITZER 3.6um IRAC (Band 1) magnitude real 4 mag    
mag3_6_err glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 3.6um IRAC (Band 1) 1 sigma error real 4 mag 99.999  
mag3_err glimpse_hrc_inter, glimpse_mca_inter GLIMPSE 1sigma mag error (IRAC band 3) real 4 mag 99.999  
mag4 glimpse_hrc_inter, glimpse_mca_inter GLIMPSE Magnitude in IRAC band 4 real 4 mag 99.999  
mag4_5 glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 4.5um IRAC (Band 2) magnitude real 4 mag 99.999  
mag4_5 sage_lmcIracSource SPITZER 4.5um magnitude real 4 mag    
mag4_5 sage_smcIRACv1_5Source SPITZER 4.5um IRAC (Band 1) magnitude real 4 mag    
mag4_5_err glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 4.5um IRAC (Band 2) 1 sigma error real 4 mag 99.999  
mag4_err glimpse_hrc_inter, glimpse_mca_inter GLIMPSE 1sigma mag error (IRAC band 4) real 4 mag 99.999  
mag5_8 glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 5.8um IRAC (Band 3) magnitude real 4 mag 99.999  
mag5_8 sage_lmcIracSource SPITZER 5.8um magnitude real 4 mag    
mag5_8 sage_smcIRACv1_5Source SPITZER 5.8um IRAC (Band 1) magnitude real 4 mag    
mag5_8_err glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 5.8um IRAC (Band 3) 1 sigma error real 4 mag 99.999  
mag70 sage_lmcMips70Source SPITZER 70um magnitude float 8 mag    
mag8_0 glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 8.0um IRAC (Band 4) magnitude real 4 mag 99.999  
mag8_0 sage_lmcIracSource SPITZER 8.0um magnitude real 4 mag    
mag8_0 sage_smcIRACv1_5Source SPITZER 8.0um IRAC (Band 1) magnitude real 4 mag    
mag8_0_err glimpse1_hrc, glimpse1_mca, glimpse2_hrc, glimpse2_mca GLIMPSE 8.0um IRAC (Band 4) 1 sigma error real 4 mag 99.999  
MAG_1 agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss SIXDF supplied magnitude 1 real 4 mag    
MAG_1 supercos SIXDF Bj mag, SuperCOSMOS magnitudes generated from a revised calibration , done at the time of input catalogue preparation, tuned to the mag range of interest. real 4 mag    
MAG_1 twomass SIXDF supplied magnitude real 4 mag    
MAG_2 agntwomass, denisi, denisj, durukst, fsc, hes, hipass, nvss, rass, shapley, sumss SIXDF supplied magnitude 2 real 4 mag    
MAG_2 supercos SIXDF R mag, SuperCOSMOS magnitudes generated from a revised calibration, done at the time of input catalogue preparation, tuned to the mag range of interest. real 4 mag    
MAG_2 twomass SIXDF supplied magnitude real 4 mag    
MAG_3 agntwomass SIXDF supplied magnitude 3 real 4      
MAG_AUTO mgcDetection MGC 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
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 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 ThreeDimExtinctionMaps EXTINCT UID of the map tinyint 1     meta.id;meta.main
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 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      
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 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 VHSv20171207 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VHSv20180419 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIDEODR2 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIDEODR3 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIDEODR4 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIDEODR5 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIDEOv20100513 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIDEOv20111208 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGDR2 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGDR3 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGDR4 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20110714 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20111019 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20130417 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20140402 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20150421 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20151230 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20160406 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20161202 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20170715 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VIKINGv20181012 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCDR1 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCDR2 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCDR3 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCDR4 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20110816 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20110909 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20120126 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20121128 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20130304 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20130805 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20140428 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20140903 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20150309 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20151218 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20160311 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20160822 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20170109 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20170411 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20171101 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20180702 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VMCv20181120 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID Multiframe VVVDR4 UID of library object mask frame {image extension keyword: CIR_OPM} bigint 8   -99999999 obs.field
maskID ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC UID of library object mask frame bigint 8   -99999999 obs.field
masktype Multiframe 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 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 VHSv20171207 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VHSv20180419 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIDEODR2 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIDEODR3 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIDEODR4 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIDEODR5 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIDEOv20100513 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIDEOv20111208 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGDR2 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGDR3 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGDR4 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20110714 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20111019 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20130417 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20140402 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20150421 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20151230 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20160406 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20161202 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20170715 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VIKINGv20181012 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCDR1 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCDR2 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCDR3 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCDR4 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20110816 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20110909 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20120126 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20121128 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20130304 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20130805 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20140428 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20140903 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20150309 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20151218 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20160311 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20160822 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20170109 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20170411 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20171101 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20180702 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VMCv20181120 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype Multiframe VVVDR4 Mask type {image primary HDU keyword: MASKTYPE} tinyint 1   0  
masktype ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Mask type tinyint 1   0  
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 VHSv20171207 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, vhsSourceXtwompzPhotoz, vhsSourceXwiseScosPhotoz VHSv20170630 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 videoSourceNeighbours VIDEODR5 The unique ID in videoSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID videoSourceNeighbours VIDEOv20100513 The unique ID in videoSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID videoSourceNeighbours VIDEOv20111208 The unique ID in videoSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID videoSourceNeighbours, videoSourceXDetection, videoSourceXSSASource, videoSourceXStripe82PhotoObjAll, videoSourceXtwomass_psc, videoSourceXtwomass_xsc, videoSourceXwise_prelimsc VIDEODR2 The unique ID in videoSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID videoSourceNeighbours, videoSourceXallwise_sc VIDEODR4 The unique ID in videoSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID videoSourceNeighbours, videoSourceXwise_allskysc VIDEODR3 The unique ID in videoSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours VIKINGDR4 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours VIKINGv20110714 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours VIKINGv20111019 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours VIKINGv20130417 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours VIKINGv20140402 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours VIKINGv20151230 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours VIKINGv20160406 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours, vikingSourceXAtlasDR1Source, vikingSourceXwise_allskysc VIKINGDR3 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours, vikingSourceXDR5lasSource, vikingSourceXDR7PhotoObj, vikingSourceXDR7PhotoObjAll, vikingSourceXDetection, vikingSourceXSSASource, vikingSourceXStripe82PhotoObjAll, vikingSourceXgrs_ngpSource, vikingSourceXgrs_ranSource, vikingSourceXgrs_sgpSource, vikingSourceXmgcDetection, vikingSourceXtwomass_psc, vikingSourceXtwomass_xsc, vikingSourceXwise_prelimsc VIKINGDR2 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours, vikingSourceXGDR1gaia_source, vikingSourceXGDR1tgas_source VIKINGv20170715 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours, vikingSourceXGDR1tgas_source, vikingSourceXGDR2gaia_source, vikingSourceXallwise_sc, vikingSourceXfirstSource VIKINGv20181012 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours, vikingSourceXallwise_sc VIKINGv20150421 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vikingSourceNeighbours, vikingSourceXtwompzPhotoz, vikingSourceXwiseScosPhotoz VIKINGv20161202 The unique ID in vikingSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcPsfCatalogueXGDR1gaia_source VMCv20171101 The unique ID in vmcPsfCatalogue (=psfID) bigint 8     meta.id;meta.main
masterObjID vmcPsfCatalogueXGDR1gaia_source, vmcPsfCatalogueXGDR1tgas_source, vmcPsfCatalogueXSSASource, vmcPsfCatalogueXakari_lmc_psa_v1, vmcPsfCatalogueXakari_lmc_psc_v1, vmcPsfCatalogueXallwise_sc, vmcPsfCatalogueXdenisDR3Source, vmcPsfCatalogueXeros2LMCSource, vmcPsfCatalogueXeros2SMCSource, vmcPsfCatalogueXerosLMCSource, vmcPsfCatalogueXerosSMCSource, vmcPsfCatalogueXmachoLMCSource, vmcPsfCatalogueXmachoSMCSource, vmcPsfCatalogueXmcps_lmcSource, vmcPsfCatalogueXmcps_smcSource, vmcPsfCatalogueXogle3LpvLmcSource, vmcPsfCatalogueXogle3LpvSmcSource, vmcPsfCatalogueXogle4CepLmcSource, vmcPsfCatalogueXogle4CepSmcSource, vmcPsfCatalogueXogle4RRLyrLmcSource, vmcPsfCatalogueXogle4RRLyrSmcSource, vmcPsfCatalogueXsage_lmcIracSource, vmcPsfCatalogueXsage_lmcMips160Source, vmcPsfCatalogueXsage_lmcMips24Source, vmcPsfCatalogueXsage_lmcMips70Source, vmcPsfCatalogueXspitzer_smcSource, vmcPsfCatalogueXtwomass_psc, vmcPsfCatalogueXtwomass_sixx2_psc, vmcPsfCatalogueXtwomass_sixx2_xsc, vmcPsfCatalogueXtwomass_xsc, vmcPsfCatalogueXtwompzPhotoz, vmcPsfCatalogueXwiseScosPhotoz, vmcPsfCatalogueXwise_allskysc VMCv20170411 The unique ID in vmcPsfCatalogue (=psfID) bigint 8     meta.id;meta.main
masterObjID vmcPsfDetectionsXGDR1gaia_source VMCv20181120 The unique ID in vmcPsfDetections (=psfID) bigint 8     meta.id;meta.main
masterObjID vmcPsfDetectionsXGDR1gaia_source, vmcPsfDetectionsXGDR1tgas_source, vmcPsfDetectionsXGDR2gaia_source, vmcPsfDetectionsXSSASource, vmcPsfDetectionsXakari_lmc_psa_v1, vmcPsfDetectionsXakari_lmc_psc_v1, vmcPsfDetectionsXallwise_sc, vmcPsfDetectionsXdenisDR3Source, vmcPsfDetectionsXeros2LMCSource, vmcPsfDetectionsXeros2SMCSource, vmcPsfDetectionsXerosLMCSource, vmcPsfDetectionsXerosSMCSource, vmcPsfDetectionsXmachoLMCSource, vmcPsfDetectionsXmachoSMCSource, vmcPsfDetectionsXmcps_lmcSource, vmcPsfDetectionsXmcps_smcSource, vmcPsfDetectionsXogle3LpvLmcSource, vmcPsfDetectionsXogle3LpvSmcSource, vmcPsfDetectionsXogle4CepLmcSource, vmcPsfDetectionsXogle4CepSmcSource, vmcPsfDetectionsXogle4RRLyrLmcSource, vmcPsfDetectionsXogle4RRLyrSmcSource, vmcPsfDetectionsXravedr5Source, vmcPsfDetectionsXsage_lmcIracSource, vmcPsfDetectionsXsage_lmcMips160Source, vmcPsfDetectionsXsage_lmcMips24Source, vmcPsfDetectionsXsage_lmcMips70Source, vmcPsfDetectionsXspitzer_smcSource, vmcPsfDetectionsXtwomass_psc, vmcPsfDetectionsXtwomass_sixx2_psc, vmcPsfDetectionsXtwomass_sixx2_xsc, vmcPsfDetectionsXtwomass_xsc, vmcPsfDetectionsXtwompzPhotoz, vmcPsfDetectionsXwiseScosPhotoz, vmcPsfDetectionsXwise_allskysc VMCv20180702 The unique ID in vmcPsfDetections (=psfID) bigint 8     meta.id;meta.main
masterObjID vmcPsfSourceXGDR1gaia_source VMCv20181120 The unique ID in vmcPsfSource (=psfSourceID) bigint 8     meta.id;meta.main
masterObjID vmcPsfSourceXGDR1gaia_source, vmcPsfSourceXGDR1tgas_source, vmcPsfSourceXGDR2gaia_source, vmcPsfSourceXSSASource, vmcPsfSourceXakari_lmc_psa_v1, vmcPsfSourceXakari_lmc_psc_v1, vmcPsfSourceXallwise_sc, vmcPsfSourceXdenisDR3Source, vmcPsfSourceXeros2LMCSource, vmcPsfSourceXeros2SMCSource, vmcPsfSourceXerosLMCSource, vmcPsfSourceXerosSMCSource, vmcPsfSourceXmachoLMCSource, vmcPsfSourceXmachoSMCSource, vmcPsfSourceXmcps_lmcSource, vmcPsfSourceXmcps_smcSource, vmcPsfSourceXogle3LpvLmcSource, vmcPsfSourceXogle3LpvSmcSource, vmcPsfSourceXogle4CepLmcSource, vmcPsfSourceXogle4CepSmcSource, vmcPsfSourceXogle4RRLyrLmcSource, vmcPsfSourceXogle4RRLyrSmcSource, vmcPsfSourceXravedr5Source, vmcPsfSourceXsage_lmcIracSource, vmcPsfSourceXsage_lmcMips160Source, vmcPsfSourceXsage_lmcMips24Source, vmcPsfSourceXsage_lmcMips70Source, vmcPsfSourceXspitzer_smcSource, vmcPsfSourceXtwomass_psc, vmcPsfSourceXtwomass_sixx2_psc, vmcPsfSourceXtwomass_sixx2_xsc, vmcPsfSourceXtwomass_xsc, vmcPsfSourceXtwompzPhotoz, vmcPsfSourceXwiseScosPhotoz, vmcPsfSourceXwise_allskysc VMCv20180702 The unique ID in vmcPsfSource (=psfSourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20110816 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20110909 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20120126 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20121128 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20130304 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20130805 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20140903 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20150309 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20151218 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20160311 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20170109 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20171101 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours VMCv20181120 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXGDR1gaia_source, vmcSourceXGDR1tgas_source VMCv20170411 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXGDR2gaia_source, vmcSourceXPsfDetections, vmcSourceXPsfSource, vmcSourceXravedr5Source VMCv20180702 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXPsfCatalogue, vmcSourceXVariablesType, vmcSourceXeros2LMCSource, vmcSourceXeros2SMCSource, vmcSourceXwise_allskysc VMCDR2 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXSSASource, vmcSourceXSynopticSource, vmcSourceXdenisDR3Source, vmcSourceXerosLMCSource, vmcSourceXerosSMCSource, vmcSourceXmachoLMCSource, vmcSourceXmachoSMCSource, vmcSourceXmcps_lmcSource, vmcSourceXmcps_smcSource, vmcSourceXsage_lmcIracSource, vmcSourceXsage_lmcMips160Source, vmcSourceXsage_lmcMips24Source, vmcSourceXsage_lmcMips70Source, vmcSourceXspitzer_smcSource, vmcSourceXtwomass_psc, vmcSourceXtwomass_sixx2_psc, vmcSourceXtwomass_sixx2_xsc, vmcSourceXtwomass_xsc, vmcSourceXwise_prelimsc VMCDR1 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXakari_lmc_psa_v1, vmcSourceXakari_lmc_psc_v1 VMCDR3 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXallwise_sc VMCDR4 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXogle3LpvLmcSource, vmcSourceXogle3LpvSmcSource, vmcSourceXogle4CepLmcSource, vmcSourceXogle4CepSmcSource, vmcSourceXogle4RRLyrLmcSource, vmcSourceXogle4RRLyrSmcSource, vmcSourceXtwompzPhotoz, vmcSourceXwiseScosPhotoz VMCv20160822 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vmcSourceNeighbours, vmcSourceXxmm3dr4 VMCv20140428 The unique ID in vmcSource (=sourceID) bigint 8     meta.id;meta.main
masterObjID vvvPsfDaophotJKsSourceXDR8gpsSource, vvvPsfDaophotJKsSourceXSSASource, vvvPsfDaophotJKsSourceXallwise_sc, vvvPsfDaophotJKsSourceXgaia_source, vvvPsfDaophotJKsSourceXglimpse1_hrc, vvvPsfDaophotJKsSourceXglimpse1_mca, vvvPsfDaophotJKsSourceXglimpse2_hrc, vvvPsfDaophotJKsSourceXglimpse2_mca, vvvPsfDaophotJKsSourceXiras_psc, vvvPsfDaophotJKsSourceXtgas_source, vvvPsfDaophotJKsSourceXtwomass_psc, vvvPsfDaophotJKsSourceXtwomass_sixx2_psc, vvvPsfDaophotJKsSourceXtwomass_sixx2_xsc, vvvPsfDaophotJKsSourceXtwomass_xsc, vvvPsfDaophotJKsSourceXwise_allskysc, vvvPsfDaophotJKsSourceXxmm3dr4 VVVDR4 The unique ID in vvvPsfDaophotJKsSource (=psfID) bigint 8     meta.id;meta.main
masterObjID vvvPsfDophotZYJHKsSourceXDR8gpsSource, vvvPsfDophotZYJHKsSourceXSSASource, vvvPsfDophotZYJHKsSourceXallwise_sc, vvvPsfDophotZYJHKsSourceXgaia_source, vvvPsfDophotZYJHKsSourceXglimpse1_hrc, vvvPsfDophotZYJHKsSourceXglimpse1_mca, vvvPsfDophotZYJHKsSourceXglimpse2_hrc, vvvPsfDophotZYJHKsSourceXglimpse2_mca, vvvPsfDophotZYJHKsSourceXiras_psc, vvvPsfDophotZYJHKsSourceXtgas_source, vvvPsfDophotZYJHKsSourceXtwomass_psc, vvvPsfDophotZYJHKsSourceXtwomass_sixx2_psc, vvvPsfDophotZYJHKsSourceXtwomass_sixx2_xsc, vvvPsfDophotZYJHKsSourceXtwomass_xsc, vvvPsfDophotZYJHKsSourceXwise_allskysc, vvvPsfDophotZYJHKsSourceXxmm3dr4 VVVDR4 The unique ID in vvvPsfDophotZYJHKsSource (=psfID) bigint 8     meta.id;meta.main
masterObjID vvvSourceXDR8gpsSource, vvvSourceXDetection, vvvSourceXGDR1gaia_source, vvvSourceXGDR1tgas_source, vvvSourceXParallaxCatalogue, vvvSourceXProperMotionCatalogue, vvvSourceXPsfDaophotJKsSource, vvvSourceXPsfDophotZYJHKsSource, vvvSourceXSSASource, vvvSourceXSynopticSource, vvvSourceXglimpse1_hrc, vvvSourceXglimpse1_mca, vvvSourceXglimpse2_hrc, vvvSourceXglimpse2_mca, vvvSourceXiras_psc, vvvSourceXtwomass_psc, vvvSourceXtwomass_xsc, vvvSourceXwise_allskysc, vvvSourceXxmm3dr4 VVVDR4 The unique ID in vvvSource (=sourceID) bigint 8     meta.id;meta.main
MATCH_1XMM twoxmm, twoxmm_v1_2 XMM The IAU name of the 1XMM source ID matched within radius of 3 arcsec and using the closest candidate. varchar 21      
MATCH_2XMMP twoxmm, twoxmm_v1_2 XMM The IAU name of the 2XMMp source ID matched within radius of 3" and using the closest candidate. varchar 22      
MATCH_DR spectra SIXDF position match error (arcsec) float 8 arcsec    
matched_observations gaia_source GAIADR2 The number of observations matched to this source smallint 2     meta.number
matched_observations gaia_source, tgas_source GAIADR1 Amount of observations matched to this source smallint 2     meta.number
MatchFlag_2MASS ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 3     meta.code
MatchFlag_ALLWISE ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 3     meta.code
MatchFlag_APASSDR9 ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 5     meta.code
MatchFlag_DENIS ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 5     meta.code
MatchFlag_PPMXL ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 3     meta.code
MatchFlag_TGAS ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 5     meta.code
MatchFlag_TYCHO2 ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 5     meta.code
MatchFlag_UCAC4 ravedr5Source RAVE Crossmatch quality flag (Note 7, DR5) varchar 3     meta.code
MatchFlag_USNOB1 ravedr5Source RAVE Crossmatch quality flag varchar 3     meta.code
MATL15 akari_lmc_psa_v1, akari_lmc_psc_v1 AKARI the number of matching with L15
merging the 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 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 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 VHSv20171207 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VHSv20180419 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIDEODR2 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIDEODR3 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIDEODR4 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIDEODR5 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIDEOv20100513 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIDEOv20111208 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGDR2 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGDR3 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGDR4 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20110714 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20111019 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20130417 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20140402 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20150421 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20151230 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20160406 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20161202 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20170715 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VIKINGv20181012 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCDR1 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCDR2 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCDR3 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCDR4 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20110816 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20110909 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20120126 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20121128 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20130304 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20130805 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20140428 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20140903 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20150309 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20151218 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20160311 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20160822 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20170109 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20170411 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20171101 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20180702 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VMCv20181120 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec CurrentAstrometry VVVDR4 The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxDec ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC The maximum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
maxGalLat ThreeDimExtinctionMaps EXTINCT Maximum Galactic Latitude float 8 Degrees   stat.max;pos.galactic.lat
maxGalLong ThreeDimExtinctionMaps EXTINCT Maximum Galactic Longitude float 8 Degrees   stat.max;pos.galactic.lon
maximum phot_variable_time_series_g_fov_statistical_parameters GAIADR1 Maximum magnitude of the G-band time series float 8 mag   phot.mag;stat.max
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 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 VHSv20171207 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VHSv20180419 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIDEODR2 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIDEODR3 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIDEODR4 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIDEODR5 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIDEOv20111208 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGDR2 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGDR3 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGDR4 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20110714 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20111019 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20130417 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20140402 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20150421 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20151230 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20160406 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20161202 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20170715 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VIKINGv20181012 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCDR1 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCDR2 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCDR3 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCDR4 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20110816 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20110909 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20120126 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20121128 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20130304 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20130805 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20140428 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20140903 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20150309 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20151218 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20160311 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20160822 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20170109 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20170411 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20171101 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20180702 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VMCv20181120 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize Multiframe VVVDR4 SADT maximum jitter size {image primary HDU keyword: HIERARCH ESO OCS SADT MAXJIT} real 4 arcsec -0.9999995e9  
maxJitSize ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC SADT maximum jitter size real 4 arcsec -0.9999995e9  
maxMoonFli Multiframe 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 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 VHSv20171207 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VHSv20180419 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIDEODR2 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIDEODR3 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIDEODR4 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIDEODR5 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIDEOv20111208 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGDR2 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGDR3 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGDR4 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20110714 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20111019 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20130417 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20140402 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20150421 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20151230 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20160406 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20161202 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20170715 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VIKINGv20181012 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCDR1 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCDR2 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCDR3 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCDR4 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20110816 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20110909 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20120126 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20121128 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20130304 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20130805 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20140428 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20140903 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20150309 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20151218 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20160311 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20160822 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20170109 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20170411 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20171101 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20180702 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VMCv20181120 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli Multiframe VVVDR4 Requested maximum fractional lunar illumination {image primary HDU keyword: HIERARCH ESO OBS MOON FLI} real 4   -0.9999995e9  
maxMoonFli ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Requested maximum fractional lunar illumination real 4   -0.9999995e9  
maxPllx 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 vikingVariability VIKINGv20181012 Upper limit of 90% confidence interval for parallax measurement real 4 mas -0.9999995e9 pos.parallax;stat.max
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data 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 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 vvvVariability VVVDR4 Upper limit of 90% confidence interval for parallax measurement real 4 mas -0.9999995e9 pos.parallax;stat.max
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data 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 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 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 VHSv20171207 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VHSv20180419 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIDEODR2 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIDEODR3 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIDEODR4 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIDEODR5 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIDEOv20100513 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIDEOv20111208 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGDR2 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGDR3 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGDR4 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20110714 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20111019 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20130417 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20140402 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20150421 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20151230 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20160406 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20161202 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20170715 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VIKINGv20181012 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCDR1 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCDR2 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCDR3 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCDR4 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20110816 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20110909 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20120126 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20121128 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20130304 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20130805 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20140428 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20140903 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20150309 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20151218 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20160311 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20160822 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20170109 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20170411 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20171101 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20180702 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VMCv20181120 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa CurrentAstrometry VVVDR4 The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
maxRa ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC The maximum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
MC_class combo17CDFSSource COMBO17 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      
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_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 VMCv20110816 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20110909 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20120126 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20121128 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20130304 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20130805 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20140428 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20140903 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20150309 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20151218 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20160311 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20160822 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20170109 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20170411 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20171101 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20180702 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vmcSynopticMergeLog VMCv20181120 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
meanMjdObs vvvSynopticMergeLog VVVDR4 Mean modified julian date of frameset. float 8 days -0.9999995e9 time.epoch
median phot_variable_time_series_g_fov_statistical_parameters GAIADR1 Median magnitude of the 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 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 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 VHSv20171207 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VHSv20180419 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIDEODR2 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIDEODR3 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIDEODR4 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIDEODR5 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIDEOv20111208 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGDR2 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGDR3 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGDR4 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20110714 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20111019 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20130417 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20140402 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20150421 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20151230 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20160406 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20161202 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20170715 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VIKINGv20181012 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCDR1 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCDR2 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCDR3 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCDR4 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20110816 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20110909 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20120126 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20121128 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20130304 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20130805 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20140428 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20140903 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20150309 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20151218 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20160311 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20160822 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20170109 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20170411 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20171101 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20180702 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VMCv20181120 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa MultiframeDetector VVVDR4 [deg] Mean PA from N to E {catalogue extension keyword:  MED_PA} real 4   -0.9999995e9  
medPa ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector VSAQC [deg] Mean PA from N to E real 4   -0.9999995e9  
mergedClass 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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 VHSv20171207 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

mergedClass vikingSource VIKINGv20181012 Class flag from available measurements (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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

mergedClass vmcSourceRemeasurement VMCv20110816 Class flag based on list remeasurement prescription smallint 2     meta.code
mergedClass vmcSourceRemeasurement VMCv20110909 Class flag based on list remeasurement prescription smallint 2     meta.code
mergedClass vvvSource, vvvSynopticSource VVVDR4 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:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

Then, each separately available classification is combined for a merged source using Bayesian classification rules, assuming each datum is independent:

P(classk)=ΠiP(classk)i / ΣkΠiP(classk)i
where classk is one of star|galaxy|noise|saturated, and i denotes the ith single detection passband measurement available (the non-zero entries are necessary for the independent measures method to work, since some cases might otherwise be mutually exclusive). For example, if an object is classed in J|H|K as -1|-2|+1 it would have merged classification probabilities of pStar=73.5%, pGalaxy=26.2%, pNoise=0.3% and pSaturated=0.0%. Decision thresholds for the resulting discrete classification flag mergedClass are 90% for definitive and 70% for probable; hence the above example would be classified (not unreasonably) as probably a star (mergedClass=-2). An additional decision rule enforces mergedClass=-9 (saturated) when any individual classification flag indicates saturation.

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 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 VHSv20171207 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 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 vikingSource VIKINGv20181012 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 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, 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 vvvSource, vvvSynopticSource VVVDR4 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 VVVDR4 The difference in time between the start time of the last band and the first band. float 8 days -0.9999995e9  
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 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 VHSv20171207 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VHSv20180419 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIDEODR2 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIDEODR3 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIDEODR4 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIDEODR5 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIDEOv20100513 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIDEOv20111208 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGDR2 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGDR3 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGDR4 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20110714 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20111019 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20130417 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20150421 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20151230 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20160406 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20161202 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20170715 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VIKINGv20181012 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCDR1 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCDR3 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCDR4 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20110816 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20110909 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20120126 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20121128 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20130304 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20130805 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20140428 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20140903 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20150309 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20151218 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20160311 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20160822 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20170109 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20170411 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20171101 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20180702 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VMCv20181120 Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VSAQC Table name of curation log for source merging varchar 64     ??
mergeLogTable Programme VVVDR4 Table name of curation log for source merging varchar 64     ??
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 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 VHSv20171207 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vhsMergeLog VHSv20180419 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vhsMergeLog, vhsTileSet VHSDR1 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion videoMergeLog VIDEODR3 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion videoMergeLog VIDEODR4 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion videoMergeLog VIDEODR5 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion videoMergeLog VIDEOv20100513 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion videoMergeLog VIDEOv20111208 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion videoMergeLog, videoTileSet VIDEODR2 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vikingMergeLog VIKINGDR3 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGDR4 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20110714 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vikingMergeLog VIKINGv20111019 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vikingMergeLog VIKINGv20130417 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20140402 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20150421 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20151230 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20160406 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20161202 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20170715 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog VIKINGv20181012 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vikingMergeLog, vikingTileSet VIKINGDR2 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20160909 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vikingZY_selJ_RemeasMergeLog VIKINGZYSELJv20170124 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vmcMergeLog VMCDR2 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCDR3 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCDR4 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20110816 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vmcMergeLog VMCv20110909 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vmcMergeLog VMCv20120126 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vmcMergeLog VMCv20121128 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20130304 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20130805 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20140428 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20140903 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20150309 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20151218 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20160311 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20160822 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20170109 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20170411 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20171101 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20180702 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog VMCv20181120 version number of the software used to merge the frames real 4     meta.id;meta.software
mergeSwVersion vmcMergeLog, vmcSynopticMergeLog, vmcTileSet VMCDR1 version number of the software used to merge the frames real 4     meta.software
mergeSwVersion vvvMergeLog, vvvSynopticMergeLog, vvvTileSet VVVDR4 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 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 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 VHSv20171207 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VHSv20180419 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIDEODR2 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIDEODR3 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIDEODR4 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIDEODR5 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIDEOv20100513 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIDEOv20111208 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGDR2 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGDR3 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGDR4 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20110714 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20111019 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20130417 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20150421 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20151230 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20160406 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20161202 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20170715 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VIKINGv20181012 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCDR1 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCDR3 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCDR4 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20110816 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20110909 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20120126 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20121128 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20130304 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20130805 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20140428 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20140903 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20150309 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20151218 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20160311 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20160822 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20170109 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20170411 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20171101 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20180702 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VMCv20181120 CASU difference image tool option string specifying the method to employ (recommended value=adaptive/back/zerosky) varchar 64     ??
method RequiredDiffImage VVVDR4 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 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 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 VHSv20171207 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VHSv20180419 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIDEODR2 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIDEODR3 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIDEODR4 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIDEODR5 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIDEOv20100513 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIDEOv20111208 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGDR2 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGDR3 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGDR4 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20110714 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20111019 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20130417 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20140402 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20150421 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20151230 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20160406 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20161202 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20170715 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VIKINGv20181012 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCDR1 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCDR2 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCDR3 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCDR4 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20110816 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20110909 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20120126 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20121128 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20130304 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20130805 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20140428 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20140903 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20150309 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20151218 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20160311 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20160822 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20170109 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20170411 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20171101 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20180702 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VMCv20181120 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec CurrentAstrometry VVVDR4 The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minDec ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC The minimum Dec (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.dec;meta.main
minGalLat ThreeDimExtinctionMaps EXTINCT Minimum Galactic Latitude float 8 Degrees   stat.min;pos.galactic.lat
minGalLong ThreeDimExtinctionMaps EXTINCT Minimum Galactic Longitude float 8 Degrees   stat.min;pos.galactic.lon
minImageSize MultiframeDetector 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 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 VHSv20171207 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VHSv20180419 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIDEODR2 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIDEODR3 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIDEODR4 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIDEODR5 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIDEOv20100513 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIDEOv20111208 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGDR2 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGDR3 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGDR4 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20110714 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20111019 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20130417 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20140402 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20150421 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20151230 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20160406 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20161202 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20170715 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VIKINGv20181012 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCDR1 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCDR2 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCDR3 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCDR4 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20110816 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20110909 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20120126 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20121128 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20130304 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20130805 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20140428 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20140903 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20150309 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20151218 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20160311 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20160822 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20170109 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20170411 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20171101 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20180702 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VMCv20181120 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize MultiframeDetector VVVDR4 Minimum size for images (pixels) {catalogue extension keyword:  MINPIX} tinyint 1   0  
minImageSize ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector VSAQC Minimum size for images (pixels) tinyint 1   0  
minimum phot_variable_time_series_g_fov_statistical_parameters GAIADR1 Minimum magnitude of the G-band time series float 8 mag   phot.mag;stat.min
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 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 VHSv20171207 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VHSv20180419 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIDEODR2 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIDEODR3 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIDEODR4 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIDEODR5 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIDEOv20111208 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGDR2 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGDR3 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGDR4 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20110714 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20111019 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20130417 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20140402 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20150421 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20151230 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20160406 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20161202 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20170715 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VIKINGv20181012 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCDR1 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCDR2 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCDR3 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCDR4 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20110816 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20110909 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20120126 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20121128 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20130304 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20130805 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20140428 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20140903 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20150309 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20151218 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20160311 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20160822 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20170109 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20170411 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20171101 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20180702 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VMCv20181120 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist Multiframe VVVDR4 Requested minimum angular distance to the moon {image primary HDU keyword: HIERARCH ESO OBS MOON DIST} real 4 deg -0.9999995e9  
minMoonDist ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Requested minimum angular distance to the moon real 4 deg -0.9999995e9  
minor iras_psc IRAS Uncertainty ellipse minor axis smallint 2 arcsec   stat.error
minPllx 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 vikingVariability VIKINGv20181012 Lower limit of 90% confidence interval for parallax measurement real 4 mas -0.9999995e9 pos.parallax;stat.min
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data 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 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 vvvVariability VVVDR4 Lower limit of 90% confidence interval for parallax measurement real 4 mas -0.9999995e9 pos.parallax;stat.min
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data 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 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 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 VHSv20171207 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VHSv20180419 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIDEODR2 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIDEODR3 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIDEODR4 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIDEODR5 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIDEOv20100513 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIDEOv20111208 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGDR2 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGDR3 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGDR4 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20110714 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20111019 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20130417 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20140402 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20150421 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20151230 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20160406 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20161202 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20170715 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VIKINGv20181012 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCDR1 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCDR2 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCDR3 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCDR4 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20110816 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20110909 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20120126 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20121128 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20130304 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20130805 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20140428 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20140903 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20150309 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20151218 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20160311 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20160822 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20170109 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20170411 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20171101 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20180702 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VMCv20181120 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa CurrentAstrometry VVVDR4 The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
minRa ultravistaCurrentAstrometry, vhsCurrentAstrometry, videoCurrentAstrometry, vikingCurrentAstrometry, vmcCurrentAstrometry, vvvCurrentAstrometry VSAQC The minimum RA (J2000) on the device float 8 Degrees -0.9999995e9 pos.eq.ra
mjd 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 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 VHSv20171207 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vhsDetection VHSv20180419 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd videoDetection VIDEODR2 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd videoDetection VIDEODR3 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd videoDetection VIDEODR4 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd videoDetection VIDEODR5 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd videoDetection VIDEOv20111208 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vikingDetection VIKINGDR2 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vikingDetection VIKINGDR3 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGDR4 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20110714 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vikingDetection VIKINGv20111019 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vikingDetection VIKINGv20130417 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20140402 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20150421 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20151230 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20160406 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20161202 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20170715 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingDetection VIKINGv20181012 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingMapRemeasAver VIKINGZYSELJv20160909 Averaged Modified Julian Day of each detection float 8 day   time.epoch
mjd vikingMapRemeasAver VIKINGZYSELJv20170124 Averaged Modified Julian Day of each detection float 8 day   time.epoch
mjd vikingMapRemeasurement VIKINGZYSELJv20160909 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vikingMapRemeasurement VIKINGZYSELJv20170124 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCDR1 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vmcDetection VMCDR2 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCDR3 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCDR4 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20110816 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vmcDetection VMCv20110909 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vmcDetection VMCv20120126 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day    
mjd vmcDetection VMCv20121128 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20130304 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20130805 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20140428 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20140903 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20150309 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20151218 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20160311 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20160822 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20170109 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20170411 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20171101 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20180702 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vmcDetection VMCv20181120 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
mjd vvvDetection VVVDR4 The mean Modified Julian Day of each detection {catalogue TType keyword: MJDoff} float 8 day   time.epoch
MJD_FIRST xmm3dr4 XMM The MJD start date (MJD_START) of the earliest observation of any constituent detection of the unique source. float 8      
MJD_LAST xmm3dr4 XMM The MJD end date (MJD_STOP) of the last observation of any constituent detection of the unique source. float 8      
MJD_OBS ravedr5Source RAVE Modfied Julian Date float 8 day   time
MJD_START twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Modified Julian Date (i.e., JD - 2400000.5) of the start of the observation. float 8 days    
MJD_STOP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Modified Julian Date (i.e., JD - 2400000.5) of the end of the observation. float 8      
mjdEnd Multiframe 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 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 VHSv20171207 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 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 VIKINGv20181012 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 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 VVVDR4 Modified Julian Date of the observation end {image primary HDU keyword: MJD-END} float 8   -0.9999995e9 time.epoch
mjdEnd ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Modified Julian Date of the observation end float 8   -0.9999995e9 time.epoch
mjdMean MultiframeDetector 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 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 VHSv20171207 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VHSv20180419 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIDEODR2 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIDEODR3 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIDEODR4 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIDEODR5 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIDEOv20111208 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGDR2 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGDR3 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGDR4 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20110714 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20111019 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20130417 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20140402 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20150421 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20151230 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20160406 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20161202 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20170715 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VIKINGv20181012 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCDR1 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCDR2 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCDR3 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCDR4 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20110816 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20110909 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20120126 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20121128 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20130304 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20130805 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20140428 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20140903 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20150309 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20151218 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20160311 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20160822 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20170109 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20170411 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20171101 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20180702 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VMCv20181120 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean MultiframeDetector VVVDR4 Mean MJD of all images comprising this image {catalogue extension keyword:  MEANMJD} float 8 days -0.9999995e9  
mjdMean ultravistaMultiframeDetector, vhsMultiframeDetector, videoMultiframeDetector, vikingMultiframeDetector, vmcMultiframeDetector, vvvMultiframeDetector VSAQC Mean MJD of all images comprising this image float 8 days -0.9999995e9  
mjdObs Multiframe 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 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 VHSv20171207 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 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 VIKINGv20181012 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 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 VVVDR4 Modified Julian Date of the observation start {image primary HDU keyword: MJD-OBS} float 8   -0.9999995e9 time.epoch
mjdObs ultravistaMultiframe, vhsMultiframe, videoMultiframe, vikingMultiframe, vmcMultiframe, vvvMultiframe VSAQC Modified Julian Date of the observation start float 8   -0.9999995e9 time.epoch
MJDOBS_R spectra SIXDF MJD of observation float 8 Julian days    
MJDOBS_V spectra SIXDF MJD of observation float 8 Julian days    
mmmNsky160 sage_lmcMips160Source SPITZER Number of points used to determine the sky values for mmmSkymode160, mmmSigma160 and mmmSkew160 float 8      
mmmNsky24 sage_lmcMips24Source SPITZER Number of points used to determine the sky values for mmmSkymode24, mmmSigma24 and mmmSkew24 float 8      
mmmNsky70 sage_lmcMips70Source SPITZER Number of points used to determine the sky values for mmmSkymode70, mmmSigma70 and mmmSkew70 float 8      
mmmSigma160 sage_lmcMips160Source SPITZER Scalar giving standard deviation of the peak in the sky histogram float 8      
mmmSigma24 sage_lmcMips24Source SPITZER Scalar giving standard deviation of the peak in the sky histogram float 8      
mmmSigma70 sage_lmcMips70Source SPITZER Scalar giving standard deviation of the peak in the sky histogram float 8      
mmmSkew160 sage_lmcMips160Source SPITZER Scalar giving skewness of the peak in the sky histogram float 8      
mmmSkew24 sage_lmcMips24Source SPITZER Scalar giving skewness of the peak in the sky histogram float 8      
mmmSkew70 sage_lmcMips70Source SPITZER Scalar giving skewness of the peak in the sky histogram float 8      
mmmSkymode160 sage_lmcMips160Source SPITZER Scalar giving estimated mode of the sky values float 8      
mmmSkymode24 sage_lmcMips24Source SPITZER Scalar giving estimated mode of the sky values float 8      
mmmSkymode70 sage_lmcMips70Source SPITZER Scalar giving estimated mode of the sky values float 8      
modDate rosat_bsc, rosat_fsc ROSAT date when source properties were changed (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 videoSourceXDetectionBestMatch VIDEODR2 separation from expected position given astrometric model in videoSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs videoSourceXDetectionBestMatch VIDEODR3 separation from expected position given astrometric model in videoSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs videoSourceXDetectionBestMatch VIDEODR4 separation from expected position given astrometric model in videoSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs videoSourceXDetectionBestMatch VIDEODR5 separation from expected position given astrometric model in videoSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs videoSourceXDetectionBestMatch VIDEOv20100513 separation from expected position given astrometric model in videoSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs videoSourceXDetectionBestMatch VIDEOv20111208 separation from expected position given astrometric model in videoSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vikingSourceXDetectionBestMatch VIKINGDR2 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vikingSourceXDetectionBestMatch VIKINGDR3 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGDR4 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20110714 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20111019 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20130417 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20140402 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20150421 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20151230 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20160406 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20161202 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20170715 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vikingSourceXDetectionBestMatch VIKINGv20181012 separation from expected position given astrometric model in vikingSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCDR1 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCDR2 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCDR3 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCDR4 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20110816 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20110909 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20120126 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9  
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20121128 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20130304 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20130805 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20140428 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20140903 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20150309 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20151218 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20160311 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20160822 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20170109 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20170411 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20171101 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20180702 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vmcSourceXSynopticSourceBestMatch VMCv20181120 separation from expected position given astrometric model in vmcSource variability. real 4 arcsec -0.9999995e9 pos.posAng
modelDistSecs vvvSourceXDetectionBestMatch VVVDR4 separation from expected position given astrometric model in vvvSource variability. real 4 arcsec -0.9999995e9 pos.posAng
moon_lev allwise_sc2 WISE Scattered moonlight contamination flag. This is a 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      
MORPH_TYPE mgcGalaxyStruct MGC SPD's EYEBALL morphology (1=E/S0, 2=Sabc, 3=Sd/Irr, 4=dE) tinyint 1   0  
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 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 VHSv20171207 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VHSv20180419 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIDEODR2 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIDEODR3 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIDEODR4 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIDEODR5 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIDEOv20100513 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIDEOv20111208 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGDR2 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGDR3 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGDR4 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20110714 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20111019 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20130417 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20140402 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20150421 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20151230 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20160406 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20161202 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20170715 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VIKINGv20181012 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCDR1 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCDR2 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCDR3 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCDR4 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20110816 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20110909 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20120126 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20121128 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20130304 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20130805 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20140428 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20140903 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20150309 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20151218 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20160311 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20160822 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20170109 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20170411 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20171101 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20180702 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VMCv20181120 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag MultiframeDetector VVVDR4 Image morphological classifier flag, set if the classifier has been run. If so an object classification flag and a stellarness index is included in the binary table columns. {catalogue extension keyword:  CLASSIFD} tinyint 1   0 meta.code
morphClassFlag 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
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 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 VHSv20171207 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VHSv20180419 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIDEODR2 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIDEODR3 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIDEODR4 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIDEODR5 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIDEOv20100513 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIDEOv20111208 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGDR2 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGDR3 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGDR4 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20110714 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20111019 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20130417 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20150421 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20151230 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20160406 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20161202 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20170715 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VIKINGv20181012 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCDR1 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCDR3 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCDR4 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20110816 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20110909 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20120126 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20121128 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20130304 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20130805 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20140428 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20140903 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20150309 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20151218 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20160311 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20160822 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20170109 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20170411 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20171101 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20180702 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VMCv20181120 Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VSAQC Name of mosaicing tool to be used varchar 8   NONE ??
mosaicTool Programme VVVDR4 Name of mosaicing tool to be used varchar 8   NONE ??
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 vikingVarFrameSetInfo VIKINGv20181012 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 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 vvvVarFrameSetInfo VVVDR4 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.
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 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 vikingVariability VIKINGv20181012 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 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 vvvVariability VVVDR4 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.
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 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 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 VHSv20171207 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 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 VIKINGv20181012 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 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 &