OSA Browser

Glossary of OSA attributes

This Glossary alphabetically lists all attributes used in the OSAv20230621 database(s) held in the OSA. If you would like to have more information about the schema tables please use the OSAv20230621 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

P

Name Schema Table Database Description Type Length Unit Default Value Unified Content Descriptor

p1 catwise_2020, catwise_prelim WISE P vector component 1 real 4 arcsec

p1 cepheid, rrlyrae GAIADR1 Period corresponding to the maximum peak in the periodogram of G band time series float 8 days time.period

p1_error cepheid, rrlyrae GAIADR1 Uncertainty on the period corresponding to the maximum peak in the periodogram of G band time series float 8 days stat.error;time.period

p2 catwise_2020, catwise_prelim WISE P vector component 2 real 4 arcsec

PA nvssSource NVSS [-90, 90] Position angle of fitted major axis real 4 degress pos.posAng

pa atlasDetection ATLASDR1 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa atlasDetection ATLASDR3 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa atlasDetection ATLASDR4 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa atlasDetection ATLASDR5 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa atlasDetection ATLASv20131127 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa atlasDetection ATLASv20160425 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa atlasDetection ATLASv20180209 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa atlasDetection, atlasDetectionUncorr ATLASDR2 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa first08Jul16Source, firstSource, firstSource12Feb16 FIRST position angle (east of north) derived from the elliptical Gaussian model for the source real 4 degrees pos.posAng

pa vphasDetection VPHASv20160112 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vphasDetection VPHASv20170222 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa vphasDetection, vphasDetectionUncorr VPHASDR3 ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis. real 4 degrees pos.posAng

pa_2mass allwise_sc WISE Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source. This column is "null" if there is no associated 2MASS PSC source. float 8 deg

pa_2mass wise_allskysc WISE Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source, default if there is no associated 2MASS PSC source. real 4 degrees -0.9999995e9

pa_2mass wise_prelimsc WISE Position angle (degrees E of N) of the vector from the WISE source to the associated 2MASS PSC source, default if there is no associated 2MASS PSC source real 4 degrees -0.9999995e9

pairingCriterion Programme ATLASDR1 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ATLASDR2 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ATLASDR3 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ATLASDR4 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ATLASDR5 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ATLASv20131127 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ATLASv20160425 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme ATLASv20180209 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VPHASDR3 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VPHASv20160112 The pairing criterion for associating detections into merged sources real 4 Degrees ??

pairingCriterion Programme VPHASv20170222 The pairing criterion for associating detections into merged sources real 4 Degrees ??

par_pm catwise_2020, catwise_prelim WISE parallax from PM desc-asce elon real 4 arcsec

par_pm is computed from the motion-solution positions, which are translated by WPHotpmc to the standard epoch (MJD0), so except for estimation errors, par_pm is the parallax; par_pm will be null unless km = 3.

par_pmSig catwise_2020, catwise_prelim WISE one-sigma uncertainty in par_pm real 4 arcsec

par_sigma catwise_2020, catwise_prelim WISE one-sigma uncertainty in par_stat real 4 arcsec

par_stat catwise_2020, catwise_prelim WISE parallax estimate from stationary solution real 4 arcsec

The par_stat column is computed by using the motion estimate to move the ascending stationary-solution position from the ascending effective observation epoch to that of the descending solution, then dividing the ecliptic longitude difference by 2; par_stat will be null unless ka = 3 AND km > 0 AND all W?mJDmin/max/mean values are non-null in both ascending and descending mdex files.

parallax gaia_source GAIADR2 Parallax float 8 milliarcsec pos.parallax

parallax gaia_source, tgas_source GAIADR1 Parallax float 8 milliarcsec pos.parallax

parallax ravedr5Source RAVE spectrophotometric Parallax (Binney et al. 2014) real 4 mas pos.parallax

parallax_error gaia_source GAIADR2 Standard error of parallax float 8 milliarcsec stat.error;pos.parallax

parallax_error gaia_source, tgas_source GAIADR1 Standard error of parallax float 8 milliarcsec stat.error;pos.parallax

parallax_error_TGAS ravedr5Source RAVE Error of parallax float 8 mas stat.error;pos.parallax

parallax_over_error gaia_source GAIADR2 Parallax divided by standard error real 4 arith.ratio

parallax_pmdec_corr gaia_source GAIADR2 Correlation between parallax and proper motion in Declination real 4 stat.correlation;pos.parallax;pos.pm;pos.eq.dec

parallax_pmdec_corr gaia_source, tgas_source GAIADR1 Correlation between parallax and proper motion in Declination real 4 stat.correlation

parallax_pmra_corr gaia_source GAIADR2 Correlation between parallax and proper motion in Right Ascension real 4 stat.correlation;pos.parallax;pos.pm;pos.eq.ra

parallax_pmra_corr gaia_source, tgas_source GAIADR1 Correlation between parallax and proper motion in Right Ascension real 4 stat.correlation

parallax_TGAS ravedr5Source RAVE Parallax float 8 mas pos.parallax

peak_to_peak_g cepheid, rrlyrae GAIADR1 Peak-to-peak amplitude of the G band light curve float 8 mag src.var.amplitude;em.opt

peak_to_peak_g_error cepheid, rrlyrae GAIADR1 Uncertainty on peak-to-peak amplitude of the G band light curve float 8 mag stat.error;src.var.amplitude;em.opt

petroFlux atlasDetection ATLASDR1 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux atlasDetection ATLASDR3 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux atlasDetection ATLASDR4 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux atlasDetection ATLASDR5 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux atlasDetection ATLASv20131127 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux atlasDetection ATLASv20160425 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux atlasDetection ATLASv20180209 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux atlasDetection, atlasDetectionUncorr ATLASDR2 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vphasDetection VPHASv20160112 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vphasDetection VPHASv20170222 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFlux vphasDetection, vphasDetectionUncorr VPHASDR3 flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU phot.count;em.opt

petroFluxErr atlasDetection ATLASDR1 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr atlasDetection ATLASDR3 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr atlasDetection ATLASDR4 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr atlasDetection ATLASDR5 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr atlasDetection ATLASv20131127 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr atlasDetection ATLASv20160425 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr atlasDetection ATLASv20180209 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr atlasDetection, atlasDetectionUncorr ATLASDR2 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vphasDetection VPHASv20160112 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vphasDetection VPHASv20170222 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroFluxErr vphasDetection, vphasDetectionUncorr VPHASDR3 error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU stat.error

petroMag atlasDetection ATLASDR1 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag atlasDetection ATLASDR3 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag atlasDetection ATLASDR4 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag atlasDetection ATLASDR5 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag atlasDetection ATLASv20131127 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag atlasDetection ATLASv20160425 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag atlasDetection ATLASv20180209 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag atlasDetection, atlasDetectionUncorr ATLASDR2 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vphasDetection VPHASv20160112 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vphasDetection VPHASv20170222 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMag vphasDetection, vphasDetectionUncorr VPHASDR3 Calibrated Petrosian magnitude within circular aperture r_p real 4 mag phot.mag

petroMagErr atlasDetection ATLASDR1 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr atlasDetection ATLASDR3 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr atlasDetection ATLASDR4 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr atlasDetection ATLASDR5 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr atlasDetection ATLASv20131127 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr atlasDetection ATLASv20160425 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr atlasDetection ATLASv20180209 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr atlasDetection, atlasDetectionUncorr ATLASDR2 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vphasDetection VPHASv20160112 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vphasDetection VPHASv20170222 error on calibrated Petrosian magnitude real 4 mag stat.error

petroMagErr vphasDetection, vphasDetectionUncorr VPHASDR3 error on calibrated Petrosian magnitude real 4 mag stat.error

petroRad atlasDetection ATLASDR1 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad atlasDetection ATLASDR3 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad atlasDetection ATLASDR4 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad atlasDetection ATLASDR5 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad atlasDetection ATLASv20131127 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad atlasDetection ATLASv20160425 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad atlasDetection ATLASv20180209 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad atlasDetection, atlasDetectionUncorr ATLASDR2 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vphasDetection VPHASv20160112 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vphasDetection VPHASv20170222 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

petroRad vphasDetection, vphasDetectionUncorr VPHASDR3 r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels phys.angSize;src

PF_DEC mgcBrightSpec MGC PFr object declination in deg (J2000) float 8

PF_JMK mgcBrightSpec MGC PFr J-K colour from 2MASS real 4

PF_K mgcBrightSpec MGC PFr K magnitude from 2MASS real 4

PF_NAME mgcBrightSpec MGC PFr object name varchar 8

PF_R mgcBrightSpec MGC PFr R magnitude from USNO real 4

PF_RA mgcBrightSpec MGC PFr object right ascension in deg (J2000) float 8

PF_Z mgcBrightSpec MGC PFr redshift real 4

PF_ZQUAL mgcBrightSpec MGC PFr redshift quality tinyint 1

pFlag rosat_bsc, rosat_fsc ROSAT possible problem with position determination varchar 1 meta.code

pflag tycho2 GAIADR1 Mean position flag varchar 1 meta.code

pGalaxy atlasSource ATLASDR1 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy atlasSource ATLASDR2 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy atlasSource ATLASDR3 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy atlasSource ATLASDR4 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy atlasSource ATLASDR5 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy atlasSource ATLASv20131127 Probability that the source is a galaxy real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy atlasSource ATLASv20160425 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy atlasSource ATLASv20180209 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy vphasSource VPHASDR3 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy vphasSource VPHASv20160112 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pGalaxy vphasSource VPHASv20170222 Probability that the source is a galaxy real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

ph_qual allwise_sc WISE Photometric quality flag. Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio. varchar 4

A - Source is detected in this band with a flux signal-to-noise ratio w?snr>10.

B - Source is detected in this band with a flux signal-to-noise ratio 3<w?snr<10.

C - Source is detected in this band with a flux signal-to-noise ratio 2<w?snr<3.

U - Upper limit on magnitude. Source measurement has w?snr<2. The profile-fit magnitude w?mpro is a 95% confidence upper limit.

X - A profile-fit measurement was not possible at this location in this band. The value of w?mpro and w?sigmpro will be "null" in this band.

Z - A profile-fit source flux measurement was made at this location, but the flux uncertainty could not be measured. The value of w?sigmpro will be "null" in this band. The value of w?mpro will be "null" if the measured flux, w?flux, is negative, but will not be "null" if the flux is positive. If a non-null magnitude is present, it corresponds to the true flux, and not the 95% confidence upper limit. This occurs for a small number of sources found in a narrow range of ecliptic longitude which were covered by a large number of saturated pixels from 3-Band Cryo single-exposures.

ph_qual twomass_psc TWOMASS Photometric quality flag. varchar 3 meta.code.qual

ph_qual twomass_sixx2_psc TWOMASS flag indicating photometric quality of source varchar 3

ph_qual wise_allskysc WISE Photometric quality flag.
Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio. char 4

ph_qual wise_prelimsc WISE Photometric quality flag
Four character flag, one character per band [W1/W2/W3/W4], that provides a shorthand summary of the quality of the profile-fit photometry measurement in each band, as derived from the measurement signal-to-noise ratio char 4

ph_qual_ALLWISE ravedr5Source RAVE photometric quality of each band (A=highest, U=upper limit) varchar 5 meta.code_mag

phaRange rosat_bsc, rosat_fsc ROSAT PHA range with highest detection likelihood varchar 1 meta.code

pHeight atlasDetection ATLASDR1 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight atlasDetection ATLASDR3 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight atlasDetection ATLASDR4 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight atlasDetection ATLASDR5 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight atlasDetection ATLASv20131127 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight atlasDetection ATLASv20160425 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight atlasDetection ATLASv20180209 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight atlasDetection, atlasDetectionUncorr ATLASDR2 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vphasDetection VPHASv20160112 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vphasDetection VPHASv20170222 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeight vphasDetection, vphasDetectionUncorr VPHASDR3 Highest pixel value above sky {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux. real 4 ADU phot.count

pHeightErr atlasDetection ATLASDR1 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr atlasDetection ATLASDR3 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr atlasDetection ATLASDR4 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr atlasDetection ATLASDR5 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr atlasDetection ATLASv20131127 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr atlasDetection ATLASv20160425 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr atlasDetection ATLASv20180209 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr atlasDetection, atlasDetectionUncorr ATLASDR2 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vphasDetection VPHASv20160112 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vphasDetection VPHASv20170222 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

pHeightErr vphasDetection, vphasDetectionUncorr VPHASDR3 Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU stat.error

phi21_g cepheid, rrlyrae GAIADR1 Fourier decomposition parameter phi21G: phi2 - 2*phi1 (for G band) float 8 stat.Fourier

phi21_g_error cepheid, rrlyrae GAIADR1 Uncertainty on Fourier decomposition parameter phi21G float 8 stat.error

phi_opt twomass_psc TWOMASS Position angle on the sky of the vector from the the associated optical source to the TWOMASS source position, in degrees East of North. smallint 2 degrees pos.posAng

phot_bp_mean_flux gaia_source GAIADR2 Integrated BP mean flux float 8 electrons/s phot.flux;stat.mean

phot_bp_mean_flux_error gaia_source GAIADR2 Standard error on the integrated BP mean flux float 8 electrons/s stat.error;phot.flux;stat.mean

phot_bp_mean_flux_over_error gaia_source GAIADR2 Integrated mean BP flux divided by its standard error real 4 arith.ratio

phot_bp_mean_mag gaia_source GAIADR2 Integrated BP mean magnitude real 4 mag phot.mag;stat.mean

phot_bp_n_obs gaia_source GAIADR2 Number of observations contributing to BP photometry int 4 meta.number

phot_bp_rp_excess_factor gaia_source GAIADR2 Combined BP and RP excess factor real 4

phot_g_mean_flux gaia_source GAIADR2 G-band mean flux float 8 electrons/s phot.flux;stat.mean;em.opt

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

phot_g_mean_flux_error gaia_source GAIADR2 Error on G-band mean flux float 8 electrons/s stat.error;phot.flux;stat.mean;em.opt

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

phot_g_mean_flux_error_TGAS ravedr5Source RAVE Error on G-band mean flux from TGAS float 8 e-/s stat.error;phot.flux;stat.mean;em.opt

phot_g_mean_flux_over_error gaia_source GAIADR2 G-band mean flux divided by its standard error float 8 arith.ratio

phot_g_mean_flux_TGAS ravedr5Source RAVE Error on G-band mean flux from TGAS float 8 e-/s phot.flux;stat.mean;em.opt

phot_g_mean_mag aux_qso_icrf2_match, gaia_source, tgas_source GAIADR1 G-band mean magnitude float 8 mag phot.mag;stat.mean;em.opt

phot_g_mean_mag gaia_source GAIADR2 G-band mean magnitude real 4 mag phot.mag;stat.mean;em.opt

phot_g_mean_mag_TGAS ravedr5Source RAVE G-band mean magnitude from TGAS float 8 mag phot.mag;em.opt.g

phot_g_n_obs gaia_source GAIADR2 Number of observations contributing to G band photometry int 4 meta.number

phot_g_n_obs gaia_source, tgas_source GAIADR1 Number of observations contributing to G band photometry int 4 meta.number

phot_proc_mode gaia_source GAIADR2 Photometry processing mode tinyint 1 meta.code

phot_rp_mean_flux gaia_source GAIADR2 Integrated RP mean flux float 8 electrons/s phot.flux;stat.mean

phot_rp_mean_flux_error gaia_source GAIADR2 Standard error on the integrated RP mean flux float 8 electrons/s stat.error;phot.flux;stat.mean

phot_rp_mean_flux_over_error gaia_source GAIADR2 Integrated mean RP flux divided by its standard error real 4 arith.ratio

phot_rp_mean_mag gaia_source GAIADR2 Integrated RP mean magnitude real 4 mag phot.mag;stat.mean

phot_rp_n_obs gaia_source GAIADR2 Number of observations contributing to RP photometry int 4 meta.number

phot_variable_flag gaia_source GAIADR2 Photometric variability flag char 16 meta.code;src.var

phot_variable_flag gaia_source, tgas_source GAIADR1 Photometric variability flag varchar 16 meta.code;src.var

phot_variable_fundam_freq1 variable_summary GAIADR1 Fundamental frequency 1 float 8 /days src.var.pulse

photZPCat MultiframeDetector ATLASDR1 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ATLASDR2 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ATLASDR3 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ATLASDR4 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ATLASDR5 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ATLASv20131127 Photometric zero point for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ATLASv20160425 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector ATLASv20180209 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VPHASDR3 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VPHASv20160112 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat MultiframeDetector VPHASv20170222 Photometric zero point (Vega) for default extinction for the catalogue data {catalogue extension keyword: MAGZPT} real 4 mags -0.9999995e9 ??

Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log₁₀(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.

photZPCat PreviousMFDZP ATLASDR1 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ATLASDR2 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ATLASDR3 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ATLASDR4 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ATLASDR5 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ATLASv20131127 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ATLASv20160425 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP ATLASv20180209 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VPHASDR3 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VPHASv20160112 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPCat PreviousMFDZP VPHASv20170222 Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9

photZPErrCat MultiframeDetector ATLASDR1 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ATLASDR2 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ATLASDR3 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ATLASDR4 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ATLASDR5 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ATLASv20131127 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ATLASv20160425 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector ATLASv20180209 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VPHASDR3 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VPHASv20160112 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat MultiframeDetector VPHASv20170222 Photometric zero point error for the catalogue data {catalogue extension keyword: MAGZRR} real 4 mags -0.9999995e9 ??

Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.

photZPErrCat PreviousMFDZP ATLASDR1 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ATLASDR2 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ATLASDR3 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ATLASDR4 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ATLASDR5 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ATLASv20131127 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ATLASv20160425 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP ATLASv20180209 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VPHASDR3 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VPHASv20160112 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

photZPErrCat PreviousMFDZP VPHASv20170222 Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9

picoi Multiframe ATLASDR1 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ATLASDR2 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ATLASDR3 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ATLASDR4 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ATLASDR5 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ATLASv20131127 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ATLASv20160425 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe ATLASv20180209 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VPHASDR3 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VPHASv20160112 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

picoi Multiframe VPHASv20170222 PI-COI name {image primary HDU keyword: PI-COI} varchar 64 NONE

Pix_x_I denisDR3Source DENIS Pixel x position in I band float 8 pix

Pix_x_J denisDR3Source DENIS Pixel x position in J band float 8 pix

Pix_x_K denisDR3Source DENIS Pixel x position in K band float 8 pix

Pix_y_I denisDR3Source DENIS Pixel y position in I band float 8 pix

Pix_y_J denisDR3Source DENIS Pixel y position in J band float 8 pix

Pix_y_K denisDR3Source DENIS Pixel y position in K band float 8 pix

PlateNumber ravedr5Source RAVE Number of fieldplate on instrument [1..3] tinyint 1 meta.id;instr.plate

plx hipparcos_new_reduction GAIADR1 Parallax float 8 milliarcseconds pos.parallax

pm_de hipparcos_new_reduction GAIADR1 Proper motion in Declination float 8 milliarcseconds/year pos.eq.dec;pos.pm

pm_de tycho2 GAIADR1 Proper motion in Dec real 4 milliarcsec/year pos.eq.dec;pos.pm

pm_dec igsl_source GAIADR1 Proper motion in Dec at catalogue epoch real 4 milliarcsec/year pos.pm;pos.eq.dec

pm_dec_error igsl_source GAIADR1 Error in proper motion in Dec real 4 milliarcsec/year stat.error;pos.pm;pos.eq.dec

pm_ra hipparcos_new_reduction GAIADR1 Proper motion in Right Ascension float 8 milliarcseconds/year pos.eq.ra;pos.pm

pm_ra igsl_source GAIADR1 Proper motion in RA at catalogue epoch real 4 milliarcsec/year pos.pm;pos.eq.ra

pm_ra tycho2 GAIADR1 Proper motion in RA*cos(Dec) real 4 milliarcsec/year pos.eq.ra;pos.pm

pm_ra_error igsl_source GAIADR1 Error in proper motion in RA real 4 milliarcsec/year stat.error;pos.pm;pos.eq.ra

pmcode allwise_sc WISE This is a five character string that encodes information about factors that impact the accuracy of the motion estimation. These include the original blend count, whether a blend-swap took place, and the distance in hundredths of an arcsecond between the non-motion position and the motion mean-observation-epoch position. This column is null if a motion solution was not attempted or a valid solution was not found. varchar 5

The format is NQDDD where N is the original blend count, Q is either "Y" or "N" for "yes" or "no" a blend-swap occurred (i.e., the original primary component was not the final primary component), and DDD is the radial distance between the non-motion and motion at mean-observation epoch positions in units of 0.01 arcsec, clipped at 999 (almost 10 arcsec).
For example, a well-behaved source that is not part of a blend and that has similar stationary and motion fit positions would have a pmcode value like "1N008". A source with a questionable motion estimate that is passively deblended (nb=2) and whose stationary-fit and motion position differ by a significant amount would have a pmcode value like "3Y234".

pmcode catwise_2020, catwise_prelim WISE quality of the PM solution varchar 5

pmDE_error_TGAS ravedr5Source RAVE Error of proper motion (DE) float 8 mas/yr stat.error;pos.pm;pos.eq.dec

pmDE_PPMXL ravedr5Source RAVE Proper Motion (Declination) real 4 mas/yr pos.pm

pmDE_TGAS ravedr5Source RAVE Proper motion (Declination) float 8 mas/yr pos.pm;pos.eq.dec

pmDE_TYCHO2 ravedr5Source RAVE Proper motion (Declination) real 4 mas/yr pos.pm;pos.eq.dec

pmDE_UCAC4 ravedr5Source RAVE Proper Motion (Declination) real 4 mas/yr pos.pm

pmDE_USNOB1 ravedr5Source RAVE Proper Motion (Declination) real 4 mas/yr pos.pm

PMDec catwise_2020, catwise_prelim WISE proper motion in dec real 4 arcsec/yr

pmDec ukirtFSstars ATLASDR1 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars ATLASDR2 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars ATLASDR3 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars ATLASDR4 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars ATLASDR5 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars ATLASv20131127 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars ATLASv20160425 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars ATLASv20180209 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars VPHASDR3 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars VPHASv20160112 Proper motion in Dec real 4 arcsec per year 0.0

pmDec ukirtFSstars VPHASv20170222 Proper motion in Dec real 4 arcsec per year 0.0

pmdec allwise_sc WISE The apparent motion in declination estimated for this source. This column is null if the motion fit failed to converge or was not attempted. CAUTION: This is the total motion in declination, and not the proper motion. The apparent motion can be significantly affected by parallax for nearby objects. int 4 mas/year

pmdec gaia_source GAIADR2 Proper motion in Declination direction float 8 milliarcsec/year pos.pm;.pos.eq.dec

pmdec gaia_source, tgas_source GAIADR1 Proper motion in Declination direction float 8 milliarcsec/year pos.pm;.pos.eq.dec

pmdec_error gaia_source GAIADR2 Error of proper motion in Declination direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.dec

pmdec_error gaia_source, tgas_source GAIADR1 Error of proper motion in Declination direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.dec

PMRA catwise_2020, catwise_prelim WISE motion in ra real 4 arcsec/yr

pmRA ukirtFSstars ATLASDR1 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars ATLASDR2 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars ATLASDR3 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars ATLASDR4 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars ATLASDR5 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars ATLASv20131127 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars ATLASv20160425 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars ATLASv20180209 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars VPHASDR3 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars VPHASv20160112 Proper motion in RA real 4 arcsec per year 0.0

pmRA ukirtFSstars VPHASv20170222 Proper motion in RA real 4 arcsec per year 0.0

pmra allwise_sc WISE The apparent motion in right ascension estimated for this source. This column is null if the motion fit failed to converge or was not attempted. CAUTION: This is the total motion in right ascension, and not the proper motion. The apparent motion can be significantly affected by parallax for nearby objects. int 4 mas/year

pmra gaia_source GAIADR2 Proper motion in Right Ascension direction float 8 milliarcsec/year pos.pm;.pos.eq.ra

pmra gaia_source, tgas_source GAIADR1 Proper motion in Right Ascension direction float 8 milliarcsec/year pos.pm;.pos.eq.ra

pmra_error gaia_source GAIADR2 Error of proper motion in Right Ascension direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.ra

pmra_error gaia_source, tgas_source GAIADR1 Error of proper motion in Right Ascension direction float 8 milliarcsec/year stat.error;pos.pm;.pos.eq.ra

pmRA_error_TGAS ravedr5Source RAVE Error of proper motion (RA) float 8 mas/yr stat.errror;pos.pm;pos.eq.ra

pmra_pmdec_corr gaia_source GAIADR2 Correlation between proper motion in Right Ascension and proper motion in Declination real 4 stat.correlation;pos.pm;pos.eq.ra;pos.pm;pos.eq.dec

pmra_pmdec_corr gaia_source, tgas_source GAIADR1 Correlation between proper motion in Right Ascension and proper motion in Declination real 4 stat.correlation

pmRA_PPMXL ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

pmRA_TGAS ravedr5Source RAVE Proper motion (Right Ascension) float 8 mas/yr pos.pm;pos.eq.ra

pmRA_TYCHO2 ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

pmRA_UCAC4 ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

pmRA_USNOB1 ravedr5Source RAVE Proper Motion (Right Ascension) real 4 mas/yr pos.pm;pos.eq.ra

PN_1_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 1 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_1_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 Maximum likelihood real 4

PN_1_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 1 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_1_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 flux real 4 erg/cm**2/s

PN_1_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 flux error real 4 erg/cm**2/s

PN_1_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 Count rates real 4 counts/s

PN_1_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 1 Count rates error real 4 counts/s

PN_1_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 1 vignetting value. real 4

PN_2_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 2 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_2_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 Maximum likelihood real 4

PN_2_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 2 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_2_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 flux real 4 erg/cm**2/s

PN_2_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 flux error real 4 erg/cm**2/s

PN_2_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 Count rates real 4 counts/s

PN_2_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 2 Count rates error real 4 counts/s

PN_2_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 2 vignetting value. real 4

PN_3_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 3 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_3_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 Maximum likelihood real 4

PN_3_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 3 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_3_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 flux real 4 erg/cm**2/s

PN_3_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 flux error real 4 erg/cm**2/s

PN_3_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 Count rates real 4 counts/s

PN_3_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 3 Count rates error real 4 counts/s

PN_3_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 3 vignetting value. real 4

PN_4_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 4 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_4_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 Maximum likelihood real 4

PN_4_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 4 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_4_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 flux real 4 erg/cm**2/s

PN_4_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 flux error real 4 erg/cm**2/s

PN_4_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 Count rates real 4 counts/s

PN_4_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 4 Count rates error real 4 counts/s

PN_4_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 4 vignetting value. real 4

PN_5_BG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 5 background map.
Made using a 12 x 12 nodes spline fit on the source-free individual-band images. real 4 counts/pixel

PN_5_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 Maximum likelihood real 4

PN_5_EXP twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 5 exposure map, combining the mirror vignetting, detector efficiency, bad pixels and CCD gaps.
The PSF weighted mean of the area of the subimages (radius 60 arcseconds) in the individual-band exposure maps. real 4 s

PN_5_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 flux real 4 erg/cm**2/s

PN_5_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 flux error real 4 erg/cm**2/s

PN_5_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 Count rates real 4 counts/s

PN_5_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 5 Count rates error real 4 counts/s

PN_5_VIG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN band 5 vignetting value. real 4

PN_8_CTS twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Combined band source counts real 4 counts

PN_8_CTS_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Combined band source counts 1 σ error real 4 counts

PN_8_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 Maximum likelihood real 4

PN_8_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 flux real 4 erg/cm**2/s

PN_8_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 flux error real 4 erg/cm**2/s

PN_8_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 Count rates real 4 counts/s

PN_8_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 8 Count rates error real 4 counts/s

PN_9_DET_ML twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 Maximum likelihood real 4

PN_9_FLUX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 flux real 4 erg/cm**2/s

PN_9_FLUX_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 flux error real 4 erg/cm**2/s

PN_9_RATE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 Count rates real 4 counts/s

PN_9_RATE_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM PN band 9 Count rates error real 4 counts/s

PN_CHI2PROB twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM The Chi² probability (based on the null hypothesis) that the source as detected by the PN camera is constant.
The Pearson approximation to Chi² for Poissonian data was used, in which the model is used as the estimator of its own variance . If more than one exposure (that is, time series) is available for this source the smallest value of probability was used. real 4

PN_CHI2PROB xmm3dr4 XMM The Chi² probability (based on the null hypothesis) that the source as detected by the PN camera is constant.
The Pearson approximation to Chi² for Poissonian data was used, in which the model is used as the estimator of its own variance . If more than one exposure (that is, time series) is available for this source the smallest value of probability was used. float 8

PN_FILTER twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM PN filter. The options are Thick, Medium, Thin1, Thin2, and Open, depending on the efficiency of the optical blocking. varchar 6

PN_FILTER xmm3dr4 XMM PN filter. The options are Thick, Medium, Thin1, Thin2, and Open, depending on the efficiency of the optical blocking. varchar 50

PN_FLAG twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM PN flag string made of the flags 1 - 12 (counted from left to right) for the PN source detection.
In case where the camera was not used in the source detection a dash is given. In case a source was not detected by the PN the flags are all set to False (default). Flag 10 is not used. varchar 12

PN_FLAG xmm3dr4 XMM PN flag string made of the flags 1 - 12 (counted from left to right) for the PN source detection.
In case where the camera was not used in the source detection a dash is given. In case a source was not detected by the PN the flags are all set to False (default). Flag 10 is not used. varchar 50

PN_FVAR xmm3dr4 XMM The fractional excess variance measured in the PN timeseries of the detection. Where multiple PN exposures exist, it is for the one giving the largest probability of variability (PN_CHI2PROB). This quantity provides a measure of the amplitude of variability in the timeseries, above purely statistical fluctuations. float 8

PN_FVARERR xmm3dr4 XMM The error on the fractional excess variance for the PN timeseries of the detection (PN_FVAR). float 8

PN_HR1 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 1 & 2
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR1_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 1 & 2 real 4

PN_HR2 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 2 & 3
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR2_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 2 & 3 real 4

PN_HR3 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 3 & 4
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR3_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 3 & 4 real 4

PN_HR4 twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN hardness ratio between the bands 4 & 5
In the case where the rate in one band is 0.0 (i.e., too faint to be detected in this band) the hardness ratio will be -1 or +1 which is only a lower or upper limit, respectively. real 4

PN_HR4_ERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The 1 σ error of the PN hardness ratio between the bands 4 & 5 real 4

PN_MASKFRAC twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PSF weighted mean of the detector coverage of a detection as derived from the detection mask.
Sources which have less than 0.15 of their PSF covered by the detector are considered as being not detected. real 4

PN_OFFAX twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN offaxis angle (the distance between the detection position and the onaxis position on the respective detector).
The offaxis angle for a camera can be larger than 15 arcminutes when the detection is located outside the FOV of that camera. real 4 arcmin

PN_ONTIME twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM The PN ontime value (the total good exposure time (after GTI filtering) of the CCD where the detection is positioned).
If a source position falls into CCD gaps or outside of the detector it will have a NULL given. real 4 s

PN_SUBMODE twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0 XMM PN observing mode. The options are full frame mode with the full FOV exposed (in two sub-modes), and large window mode with only parts of the FOV exposed. varchar 23

PN_SUBMODE xmm3dr4 XMM PN observing mode. The options are full frame mode with the full FOV exposed (in two sub-modes), and large window mode with only parts of the FOV exposed. varchar 50

pNearH iras_psc IRAS Number of nearby hours-confirmed point sources tinyint 1 meta.number

pNearW iras_psc IRAS Number of nearby weeks-confirmed point sources tinyint 1 meta.number

pNoise atlasSource ATLASDR1 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise atlasSource ATLASDR2 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise atlasSource ATLASDR3 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise atlasSource ATLASDR4 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise atlasSource ATLASDR5 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise atlasSource ATLASv20131127 Probability that the source is noise real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise atlasSource ATLASv20160425 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise atlasSource ATLASv20180209 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise vphasSource VPHASDR3 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise vphasSource VPHASv20160112 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pNoise vphasSource VPHASv20170222 Probability that the source is noise real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

polFlux nvssSource NVSS Integrated linearly polarized flux density real 4 mJy PHOT_FLUX_LINEAR

polPA nvssSource NVSS [-90,90] The position angle of polFlux real 4 degress POS_POS-EQ

pos iras_asc IRAS Position Angle from IRAS Source to Association (E of N) smallint 2 degrees pos.posAng

posAng iras_psc IRAS Uncertainty ellipse position angle (East of North) smallint 2 degrees pos.posAng

posAngle CurrentAstrometry ATLASDR1 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ATLASDR2 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ATLASDR3 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ATLASDR4 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ATLASDR5 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ATLASv20131127 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ATLASv20160425 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry ATLASv20180209 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VPHASDR3 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VPHASv20160112 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle CurrentAstrometry VPHASv20170222 orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng

posAngle RequiredStack ATLASDR1 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack ATLASDR2 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack ATLASDR3 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack ATLASDR4 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack ATLASDR5 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack ATLASv20131127 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack ATLASv20160425 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack ATLASv20180209 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack VPHASDR3 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack VPHASv20160112 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngle RequiredStack VPHASv20170222 Orientation of image x-axis to N-S real 4 deg -0.9999995e9

posAngleTolerance Programme ATLASDR1 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ATLASDR2 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ATLASDR3 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ATLASDR4 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ATLASDR5 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ATLASv20131127 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ATLASv20160425 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme ATLASv20180209 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VPHASDR3 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VPHASv20160112 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

posAngleTolerance Programme VPHASv20170222 The position angle tolerance used when creating deep stacks and tiles real 4 Degrees -0.9999995e9 ??

POSCOROK xmm3dr4 XMM Signifies whether catcorr obtained a statistically reliable solution or not (0 = False, 1 = True). This parameter is redundant in the sense that if REFCAT is positive, then a reliable solution was considered to have been found. bit 1

POSERR twoxmm, twoxmm_v1_2, twoxmmi_dr3_v1_0, xmm3dr4 XMM Total position uncertainty in arcseconds calculated by combining the statistical error RADEC_ERR and the systematic error SYSERR as follows: POSERR = SQRT ( RADEC_ERR² + SYSERR² ). real 4 arcsec

posflg tycho2 GAIADR1 Type of Tycho2 solution varchar 1 meta.id;stat.fit

ppErrBits atlasDetection ATLASDR1 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits atlasDetection ATLASDR3 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits atlasDetection ATLASDR4 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits atlasDetection ATLASDR5 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits atlasDetection ATLASv20131127 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits atlasDetection ATLASv20160425 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits atlasDetection ATLASv20180209 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits atlasDetection, atlasDetectionUncorr ATLASDR2 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vphasDetection VPHASv20160112 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vphasDetection VPHASv20170222 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBits vphasDetection, vphasDetectionUncorr VPHASDR3 additional WFAU post-processing error bits int 4 0 meta.code

Post-processing error quality bit flags assigned to detections in the archive curation procedure for survey data. From least to most significant byte in the 4-byte integer attribute byte 0 (bits 0 to 7) corresponds to information on generally innocuous conditions that are nonetheless potentially significant as regards the integrity of that detection; byte 1 (bits 8 to 15) corresponds to warnings; byte 2 (bits 16 to 23) corresponds to important warnings; and finally byte 3 (bits 24 to 31) corresponds to severe warnings:

Byte Bit Detection quality issue Threshold or bit mask Applies to

Decimal Hexadecimal

0 4 Deblended 16 0x00000010 All VDFS catalogues

0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues

0 7 Low confidence in default aperture 128 0x00000080 All VDFS catalogues

1 12 Lies within detector 16 region of a tile 4096 0x00001000 All catalogues from tiles

2 16 Close to saturated 65536 0x00010000 All VDFS catalogues

2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 VVV only

2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All catalogues

2 23 Lies within the underexposed strip (or "ear") of a tile 8388608 0x00800000 All catalogues from tiles

3 24 Lies within an underexposed region of a tile due to missing detector 16777216 0x01000000 All catalogues from tiles

In this way, the higher the error quality bit flag value, the more likely it is that the detection is spurious. The decimal threshold (column 4) gives the minimum value of the quality flag for a detection having the given condition (since other bits in the flag may be set also; the corresponding hexadecimal value, where each digit corresponds to 4 bits in the flag, can be easier to compute when writing SQL queries to test for a given condition). For example, to exclude all Ks band sources in the VHS having any error quality condition other than informational ones, include a predicate ... AND kppErrBits ≤ 255. See the SQL Cookbook and other online pages for further information.

ppErrBitsStatus ProgrammeFrame ATLASDR1 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ATLASDR2 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ATLASDR3 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ATLASDR4 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ATLASDR5 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ATLASv20131127 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ATLASv20160425 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame ATLASv20180209 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VPHASDR3 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VPHASv20160112 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

ppErrBitsStatus ProgrammeFrame VPHASv20170222 Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4 0

previewv Multiframe ATLASDR1 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ATLASDR2 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ATLASDR3 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ATLASDR4 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ATLASDR5 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ATLASv20131127 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ATLASv20160425 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe ATLASv20180209 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VPHASDR3 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VPHASv20160112 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

previewv Multiframe VPHASv20170222 Version of previe {image primary HDU keyword: PREVIEWV} varchar 64 NONE

priam_flags gaia_source GAIADR2 Flags from Apsis-Priam analysis bigint 8 meta.code

priFlgLb rosat_bsc, rosat_fsc ROSAT priority flag L-broad tinyint 1 meta.code

priFlgLh rosat_bsc, rosat_fsc ROSAT priority flag L-hard tinyint 1 meta.code

priFlgLs rosat_bsc, rosat_fsc ROSAT priority flag L-soft tinyint 1 meta.code

priFlgMb rosat_bsc, rosat_fsc ROSAT priority flag M-broad tinyint 1 meta.code

priFlgMh rosat_bsc, rosat_fsc ROSAT priority flag M-hard tinyint 1 meta.code

priFlgMs rosat_bsc, rosat_fsc ROSAT priority flag M-soft tinyint 1 meta.code

priOrSec atlasSource ATLASDR1 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec atlasSource ATLASDR2 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec atlasSource ATLASDR3 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec atlasSource ATLASDR4 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec atlasSource ATLASDR5 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec atlasSource ATLASv20131127 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec atlasSource ATLASv20160425 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec atlasSource ATLASv20180209 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vphasSource VPHASDR3 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vphasSource VPHASv20160112 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

priOrSec vphasSource VPHASv20170222 Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8 -99999999 meta.code

Because of the spacing of the detectors in VIRCam, and the restrictions on guide star brightness, there will always be overlap regions between adjacent frame sets. Source merging is done on a set-by-set basis; hence after source merging there are usually a small number of duplicate sources in the table. A process known as seaming takes place after source merging is complete, whereby duplicates are identified and flagged. The flagging attribute is priOrSec, and the meaning of the flag is quite simple: if a source is not found to be duplicated in overlap regions, then priOrSec=0; if a source is duplicated, then priOrSec will be set to the frameSetID of the source that should be considered the best one to use out of the set of duplicates. Presently, the choice of which is best is made on the basis of proximity to the optical axis of the camera, the assumption being that this will give the best quality image in general. So, if a particular source has a non-zero priOrSec that is set to it's own value of frameSetID, then this indicates that there is a duplicate elsewhere in the table, but this is the one that should be selected as the best (i.e. this is the primary source). On the other hand, if a source has a non-zero value of priOrSec that is set a different frameSetID than that of the source in question, then this indicates that this source should be considered as a secondary duplicate of a source who's primary is actually to be found in the frame set pointed to by that value of frameSetID. Hence, the WHERE clause for selecting out a seamless, best catalogue is of the form WHERE ... AND (priOrSec=0 OR priOrSec=frameSetID).

productID EpochFrameStatus ATLASDR4 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus ATLASDR5 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus ATLASv20160425 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus ATLASv20180209 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VPHASDR3 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VPHASv20160112 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus VPHASv20170222 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID EpochFrameStatus, ProgrammeFrame ATLASDR3 Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8 -99999999

productID RequiredDiffImage ATLASDR1 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ATLASDR2 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ATLASDR3 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ATLASDR4 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ATLASDR5 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ATLASv20131127 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ATLASv20160425 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage ATLASv20180209 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VPHASDR3 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VPHASv20160112 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredDiffImage VPHASv20170222 A unique identifier assigned to each required difference image product entry int 4 ??

productID RequiredMergeLogMultiEpoch ATLASDR4 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch ATLASDR5 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch ATLASv20160425 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch ATLASv20180209 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VPHASDR3 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VPHASv20160112 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch VPHASv20170222 A unique identifier assigned to each required stack product entry int 4 ??

productID RequiredMergeLogMultiEpoch, RequiredStack ATLASDR3 A unique identifier assigned to each required stack product entry int 4 ??

productType EpochFrameStatus ATLASDR3 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus ATLASDR4 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus ATLASDR5 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus ATLASv20160425 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus ATLASv20180209 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VPHASDR3 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VPHASv20160112 Product type (stack,tile,mosaic) varchar 16 NONE

productType EpochFrameStatus VPHASv20170222 Product type (stack,tile,mosaic) varchar 16 NONE

productType ExternalProduct ATLASDR2 The product type within the imported directory varchar 16 ??

productType ExternalProduct ATLASDR3 The product type within the imported directory varchar 16 ??

productType ExternalProduct ATLASDR4 The product type within the imported directory varchar 16 ??

productType ExternalProduct ATLASDR5 The product type within the imported directory varchar 16 ??

productType ExternalProduct ATLASv20131127 The product type within the imported directory varchar 16 ??

productType ExternalProduct ATLASv20160425 The product type within the imported directory varchar 16 ??

productType ExternalProduct ATLASv20180209 The product type within the imported directory varchar 16 ??

productType ExternalProduct VPHASDR3 The product type within the imported directory varchar 16 ??

productType ExternalProduct VPHASv20160112 The product type within the imported directory varchar 16 ??

productType ExternalProduct VPHASv20170222 The product type within the imported directory varchar 16 ??

productType RequiredMergeLogMultiEpoch ATLASDR3 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch ATLASDR4 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch ATLASDR5 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch ATLASv20160425 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch ATLASv20180209 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VPHASDR3 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VPHASv20160112 Product type (stack,tile,mosaic) varchar 16

productType RequiredMergeLogMultiEpoch VPHASv20170222 Product type (stack,tile,mosaic) varchar 16

programmeID EpochFrameStatus ATLASDR4 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus ATLASDR5 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus ATLASv20160425 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus ATLASv20180209 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VPHASDR3 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VPHASv20160112 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus VPHASv20170222 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID EpochFrameStatus, ProgrammeFrame ATLASDR3 OSA assigned programme UID {image primary HDU keyword: HIERARCH ESO OBS PROG ID} int 4 -99999999 meta.id

programmeID ExternalProduct ATLASDR4 the unique programme ID int 4 meta.id

programmeID ExternalProduct ATLASDR5 the unique programme ID int 4 meta.id

programmeID ExternalProduct ATLASv20131127 the unique programme ID int 4 meta.id

programmeID ExternalProduct ATLASv20160425 the unique programme ID int 4 meta.id

programmeID ExternalProduct ATLASv20180209 the unique programme ID int 4 meta.id

programmeID ExternalProduct VPHASDR3 the unique programme ID int 4 meta.id

programmeID ExternalProduct VPHASv20160112 the unique programme ID int 4 meta.id

programmeID ExternalProduct VPHASv20170222 the unique programme ID int 4 meta.id

programmeID ExternalProduct, ProductLinks, ProgrammeCurationHistory, ProgrammeTable, RequiredDiffImage, RequiredFilters, RequiredListDrivenProduct, RequiredNeighbours, RequiredStack ATLASDR2 the unique programme ID int 4 meta.id

programmeID ExternalProduct, RequiredMergeLogMultiEpoch ATLASDR3 the unique programme ID int 4 meta.id

programmeID ProblemFrames ATLASDR3 VSA assigned programme UID int 4 -99999999 meta.id

programmeID ProblemFrames ATLASDR4 VSA assigned programme UID int 4 -99999999 meta.id

programmeID ProblemFrames ATLASDR5 VSA assigned programme UID int 4 -99999999 meta.id

programmeID ProblemFrames ATLASv20160425 VSA assigned programme UID int 4 -99999999 meta.id

programmeID ProblemFrames ATLASv20180209 VSA assigned programme UID int 4 -99999999 meta.id

programmeID ProblemFrames VPHASDR3 VSA assigned programme UID int 4 -99999999 meta.id

programmeID ProblemFrames VPHASv20170222 VSA assigned programme UID int 4 -99999999 meta.id

programmeID Programme ATLASDR1 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ATLASDR2 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ATLASDR3 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ATLASDR4 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ATLASDR5 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ATLASv20131127 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ATLASv20160425 UID of the archived programme coded as above int 4 meta.id

programmeID Programme ATLASv20180209 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VPHASDR3 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VPHASv20160112 UID of the archived programme coded as above int 4 meta.id

programmeID Programme VPHASv20170222 UID of the archived programme coded as above int 4 meta.id

programmeID SurveyProgrammes ATLASDR1 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ATLASDR2 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ATLASDR3 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ATLASDR4 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ATLASDR5 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ATLASv20131127 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ATLASv20160425 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes ATLASv20180209 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VPHASDR3 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VPHASv20160112 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

programmeID SurveyProgrammes VPHASv20170222 OSA assigned programme UID {image primary HDU keyword: PROJECT} int 4 -99999999 meta.id

project Multiframe ATLASDR1 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ATLASDR2 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ATLASDR3 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ATLASDR4 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ATLASDR5 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ATLASv20131127 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ATLASv20160425 Time-allocation code varchar 64 NONE meta.bib

project Multiframe ATLASv20180209 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VPHASDR3 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VPHASv20160112 Time-allocation code varchar 64 NONE meta.bib

project Multiframe VPHASv20170222 Time-allocation code varchar 64 NONE meta.bib

propPeriod Programme ATLASDR1 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ATLASDR2 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ATLASDR3 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ATLASDR4 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ATLASDR5 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ATLASv20131127 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ATLASv20160425 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme ATLASv20180209 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VPHASDR3 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VPHASv20160112 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

propPeriod Programme VPHASv20170222 the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months time.period

proprietary Survey ATLASDR1 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ATLASDR2 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ATLASDR3 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ATLASDR4 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ATLASDR5 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ATLASv20131127 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ATLASv20160425 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey ATLASv20180209 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VPHASDR3 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VPHASv20160112 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

proprietary Survey VPHASv20170222 Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1 ??

prox twomass_psc, twomass_xsc TWOMASS Proximity. real 4 arcsec pos.angDistance

prox tycho2 GAIADR1 Proximity indicator smallint 2 0.1 arcsec pos.angDistance

pSaturated atlasSource ATLASDR1 Probability that the source is saturated real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated atlasSource ATLASDR2 Probability that the source is saturated real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated atlasSource ATLASDR3 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated atlasSource ATLASDR4 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated atlasSource ATLASDR5 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated atlasSource ATLASv20131127 Probability that the source is saturated real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated atlasSource ATLASv20160425 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated atlasSource ATLASv20180209 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated vphasSource VPHASDR3 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated vphasSource VPHASv20160112 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pSaturated vphasSource VPHASv20170222 Probability that the source is saturated real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

psfFitChi2 atlasDetection ATLASDR1 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 atlasDetection ATLASDR3 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 atlasDetection ATLASDR4 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 atlasDetection ATLASDR5 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 atlasDetection ATLASv20131127 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 atlasDetection ATLASv20160425 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 atlasDetection ATLASv20180209 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 atlasDetection, atlasDetectionUncorr ATLASDR2 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vphasDetection VPHASv20160112 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vphasDetection VPHASv20170222 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitChi2 vphasDetection, vphasDetectionUncorr VPHASDR3 standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4 -0.9999995e9 stat.stdev

psfFitDof atlasDetection ATLASDR1 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof atlasDetection ATLASDR3 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof atlasDetection ATLASDR4 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof atlasDetection ATLASDR5 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof atlasDetection ATLASv20131127 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof atlasDetection ATLASv20160425 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof atlasDetection ATLASv20180209 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof atlasDetection, atlasDetectionUncorr ATLASDR2 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vphasDetection VPHASv20160112 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vphasDetection VPHASv20170222 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitDof vphasDetection, vphasDetectionUncorr VPHASDR3 no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2 -9999 stat.fit.dof

psfFitX atlasDetection ATLASDR1 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX atlasDetection ATLASDR3 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX atlasDetection ATLASDR4 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX atlasDetection ATLASDR5 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX atlasDetection ATLASv20131127 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX atlasDetection ATLASv20160425 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX atlasDetection ATLASv20180209 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX atlasDetection, atlasDetectionUncorr ATLASDR2 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vphasDetection VPHASv20160112 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vphasDetection VPHASv20170222 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitX vphasDetection, vphasDetectionUncorr VPHASDR3 PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 pos.cartesian.x;instr.plate

psfFitXerr atlasDetection ATLASDR1 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr atlasDetection ATLASDR3 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr atlasDetection ATLASDR4 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr atlasDetection ATLASDR5 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr atlasDetection ATLASv20131127 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr atlasDetection ATLASv20160425 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr atlasDetection ATLASv20180209 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr atlasDetection, atlasDetectionUncorr ATLASDR2 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vphasDetection VPHASv20160112 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vphasDetection VPHASv20170222 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitXerr vphasDetection, vphasDetectionUncorr VPHASDR3 Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 stat.error

psfFitY atlasDetection ATLASDR1 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY atlasDetection ATLASDR3 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY atlasDetection ATLASDR4 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY atlasDetection ATLASDR5 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY atlasDetection ATLASv20131127 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY atlasDetection ATLASv20160425 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY atlasDetection ATLASv20180209 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY atlasDetection, atlasDetectionUncorr ATLASDR2 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vphasDetection VPHASv20160112 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vphasDetection VPHASv20170222 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitY vphasDetection, vphasDetectionUncorr VPHASDR3 PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 pos.cartesian.y;instr.plate

psfFitYerr atlasDetection ATLASDR1 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr atlasDetection ATLASDR3 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr atlasDetection ATLASDR4 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr atlasDetection ATLASDR5 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr atlasDetection ATLASv20131127 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr atlasDetection ATLASv20160425 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr atlasDetection ATLASv20180209 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr atlasDetection, atlasDetectionUncorr ATLASDR2 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vphasDetection VPHASv20160112 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vphasDetection VPHASv20170222 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFitYerr vphasDetection, vphasDetectionUncorr VPHASDR3 Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 stat.error

psfFlux atlasDetection ATLASDR1 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux atlasDetection ATLASDR3 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux atlasDetection ATLASDR4 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux atlasDetection ATLASDR5 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux atlasDetection ATLASv20131127 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux atlasDetection ATLASv20160425 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux atlasDetection ATLASv20180209 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux atlasDetection, atlasDetectionUncorr ATLASDR2 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vphasDetection VPHASv20160112 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vphasDetection VPHASv20170222 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFlux vphasDetection, vphasDetectionUncorr VPHASDR3 PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 phot.count;em.opt

psfFluxErr atlasDetection ATLASDR1 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr atlasDetection ATLASDR3 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr atlasDetection ATLASDR4 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr atlasDetection ATLASDR5 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr atlasDetection ATLASv20131127 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr atlasDetection ATLASv20160425 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr atlasDetection ATLASv20180209 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr atlasDetection, atlasDetectionUncorr ATLASDR2 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vphasDetection VPHASv20160112 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vphasDetection VPHASv20170222 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfFluxErr vphasDetection, vphasDetectionUncorr VPHASDR3 Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 stat.error

psfMag atlasDetection ATLASDR1 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag atlasDetection ATLASDR3 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag atlasDetection ATLASDR4 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag atlasDetection ATLASDR5 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag atlasDetection ATLASv20131127 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag atlasDetection ATLASv20160425 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag atlasDetection ATLASv20180209 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag atlasDetection, atlasDetectionUncorr ATLASDR2 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vphasDetection VPHASv20160112 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vphasDetection VPHASv20170222 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMag vphasDetection, vphasDetectionUncorr VPHASDR3 PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE

psfMagErr atlasDetection ATLASDR1 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr atlasDetection ATLASDR3 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr atlasDetection ATLASDR4 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr atlasDetection ATLASDR5 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr atlasDetection ATLASv20131127 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr atlasDetection ATLASv20160425 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr atlasDetection ATLASv20180209 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr atlasDetection, atlasDetectionUncorr ATLASDR2 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vphasDetection VPHASv20160112 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vphasDetection VPHASv20170222 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

psfMagErr vphasDetection, vphasDetectionUncorr VPHASDR3 Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 stat.error

pStar atlasSource ATLASDR1 Probability that the source is a star real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar atlasSource ATLASDR2 Probability that the source is a star real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar atlasSource ATLASDR3 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar atlasSource ATLASDR4 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar atlasSource ATLASDR5 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar atlasSource ATLASv20131127 Probability that the source is a star real 4 stat

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar atlasSource ATLASv20160425 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar atlasSource ATLASv20180209 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar vphasSource VPHASDR3 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar vphasSource VPHASv20160112 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pStar vphasSource VPHASv20170222 Probability that the source is a star real 4 stat.probability

Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:

Flag Meaning Probability (%)

Star Galaxy Noise Saturated

-9 Saturated 0.0 0.0 5.0 95.0

-3 Probable galaxy 25.0 70.0 5.0 0.0

-2 Probable star 70.0 25.0 5.0 0.0

-1 Star 90.0 5.0 5.0 0.0

0 Noise 5.0 5.0 90.0 0.0

+1 Galaxy 5.0 90.0 5.0 0.0

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

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

pts_key twomass_psc TWOMASS A unique identification number for the PSC source. int 4 meta.id

pts_key twomass_xsc TWOMASS key to point source data DB record. int 4 meta.id

publicDb Release ATLASDR5 the name of the SQL Server database containing the public release varchar 128 NONE ??

pxcntr twomass_psc TWOMASS The pts_key value of the nearest source in the PSC. int 4 meta.number

pxcntr twomass_xsc TWOMASS ext_key value of nearest XSC source. int 4 meta.number

pxpa twomass_psc, twomass_xsc TWOMASS The position angle on the sky of the vector from the source to the nearest neighbor in the PSC, in degrees East of North. smallint 2 degrees pos.posAng

WFAU, Institute for Astronomy,
Royal Observatory, Blackford Hill
Edinburgh, EH9 3HJ, UK

osa-support@roe.ac.uk
27/06/2023