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Glossary of WSA attributes

This Glossary alphabetically lists all attributes used in the UKIDSSDR7 database(s) held in the WSA. If you would like to have more information about the schema tables please use the UKIDSSDR7 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

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified Content Descriptor
PA nvssSource NVSS [-90, 90] Position angle of fitted major axis real 4 degress   POS_POS-ANG
pa UKIDSSDetection WSA ellipse fit orientation to x axis real 4 degrees   POS_POS-ANG
pa calDetection, calListRemeasurement WSACalib ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis.
real 4 degrees   POS_POS-ANG
pa dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis.
real 4 degrees   POS_POS-ANG
pa firstSource FIRST position angle (east of north) derived from the elliptical Gaussian model for the source real 4 degrees   POS_POS-ANG
pa ptsDetection WSATransit ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis.
real 4 degrees   POS_POS-ANG
pa udsDetection WSA ellipse fit orientation to x axis {catalogue TType keyword: Position_angle}
Angle of ellipse major axis wrt x axis counterclockwise.
real 4 degrees   POS_POS-ANG
pairingCriterion Programme WSA The pairing criterion for associating detections into merged sources real 4 Degrees   ??
pairingCriterion Programme WSACalib The pairing criterion for associating detections into merged sources real 4 Degrees   ??
pairingCriterion Programme WSATransit The pairing criterion for associating detections into merged sources real 4 Degrees   ??
parallax calVariability WSACalib Parallax of star real 4 mas -0.9999995e9  
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
parallax dxsVariability, udsVariability WSA Parallax of star real 4 mas -0.9999995e9  
The Variability table contains statistics from the set of observations of each source. At present, the mean ra and dec and the error in two tangential directions are calculated. The "ra" direction is defined as tangential to both the radial direction and the cartesian z-axis and the "dec" direction is defined as both the radial direction and the "ra" direction. Since the current model is just the mean and standard deviation of the data, then the chi-squared of the fit=1. Data from good frames across all bands go into the astrometric model determination. This will include bands in non-synoptic filters: the one observation in these bands can help. In future releases a fit will be made to the rms data as a function of magnitude in each band, as has already happened for photometric data and a motion model that incorporates proper motion (and possibly parallax) will be used. The motion model is a parameter in the VarFrameSetInfo table.
pcSysID MultiframeDetector WSA PC system identifier {image extension keyword: PCSYSID} varchar 32   NONE ??
pcSysID MultiframeDetector WSACalib PC system identifier {image extension keyword: PCSYSID} varchar 32   NONE ??
pcSysID MultiframeDetector WSATransit PC system identifier {image extension keyword: PCSYSID} varchar 32   NONE ??
petroFlux UKIDSSDetection WSA flux within circular aperture to k × r_p ; k = 2 real 4 ADU   PHOT_INTENSITY_ADU
petroFlux calDetection, calListRemeasurement WSACalib flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU   PHOT_INTENSITY_ADU
petroFlux dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU   PHOT_INTENSITY_ADU
petroFlux ptsDetection WSATransit flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU   PHOT_INTENSITY_ADU
petroFlux udsDetection WSA flux within Petrosian radius circular aperture (SE: FLUX_PETRO) {catalogue TType keyword: Petr_flux} real 4 ADU   PHOT_INTENSITY_ADU
petroFluxErr UKIDSSDetection WSA error on Petrosian flux real 4 ADU   ERROR
petroFluxErr calDetection, calListRemeasurement WSACalib error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU   ERROR
petroFluxErr dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU   ERROR
petroFluxErr ptsDetection WSATransit error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU   ERROR
petroFluxErr udsDetection WSA error on Petrosian flux (SE: FLUXERR_PETRO) {catalogue TType keyword: Petr_flux_err} real 4 ADU   ERROR
petroMag dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, UKIDSSDetection, udsDetection, udsListRemeasurement WSA Calibrated Petrosian magnitude within circular aperture r_p real 4 mag   PHOT_INT-MAG
petroMag calDetection, calListRemeasurement WSACalib Calibrated Petrosian magnitude within circular aperture r_p real 4 mag   PHOT_INT-MAG
petroMag ptsDetection WSATransit Calibrated Petrosian magnitude within circular aperture r_p real 4 mag   PHOT_INT-MAG
petroMagErr dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, UKIDSSDetection, udsDetection, udsListRemeasurement WSA error on calibrated Petrosian magnitude real 4 mag   ERROR
petroMagErr calDetection, calListRemeasurement WSACalib error on calibrated Petrosian magnitude real 4 mag   ERROR
petroMagErr ptsDetection WSATransit error on calibrated Petrosian magnitude real 4 mag   ERROR
petroRad UKIDSSDetection WSA r_p as defined in Yasuda et al. 2001 AJ 112 1104 real 4 pixels   EXTENSION_RAD
petroRad calDetection, calListRemeasurement WSACalib r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels   EXTENSION_RAD
petroRad dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels   EXTENSION_RAD
petroRad ptsDetection WSATransit r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels   EXTENSION_RAD
petroRad udsDetection WSA Petrosian radius (SE: PETRO_RADIUS*A_IMAGE) {catalogue TType keyword: Petr_radius} real 4 pixels   EXTENSION_RAD
Since <FLUX>_RADIUS is expressed in multiples of the major axis, <FLUX>_RADIUS is multiplied by A_IMAGE to convert to pixels.
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     CODE_MISC
pGalaxy calSource, calSynopticSource WSACalib Probability that the source is a galaxy real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

pGalaxy dxsJKsource, gcsPointSource, gcsZYJHKsource, gpsJHKsource, gpsPointSource, lasExtendedSource, lasPointSource, lasYJHKsource, reliableDxsSource, reliableGcsPointSource, reliableGpsPointSource, reliableLasPointSource, reliableUdsSource WSA Probability that the source is a galaxy real 4     STAT_PROP
pGalaxy dxsSource, gcsSource, gpsSource, lasSource, udsSource WSA Probability that the source is a galaxy real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

ph_qual twomass_psc 2MASS Photometric quality flag. varchar 3     CODE_QUALITY
phaRange rosat_bsc, rosat_fsc ROSAT PHA range with highest detection likelihood varchar 1     CODE_MISC
pHeight UKIDSSDetection WSA Highest pixel value above sky real 4 ADU   PHOT_COUNTS_MISC
pHeight calDetection, calListRemeasurement WSACalib 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_COUNTS_MISC
pHeight dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA 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_COUNTS_MISC
pHeight ptsDetection WSATransit 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_COUNTS_MISC
pHeight udsDetection WSA Highest pixel value above sky (SE: FLUX_MAX) {catalogue TType keyword: Peak_height}
In counts relative to local value of sky - also zeroth order aperture flux.
real 4 ADU   PHOT_COUNTS_MISC
pHeightErr UKIDSSDetection WSA Error in peak height real 4 ADU   ERROR
pHeightErr calDetection, calListRemeasurement WSACalib Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU   ERROR
pHeightErr dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU   ERROR
pHeightErr ptsDetection WSATransit Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU   ERROR
pHeightErr udsDetection WSA Error in peak height {catalogue TType keyword: Peak_height_err}
FLUX_MAX*FLUXERR_APER1 / FLUX_APER1
real 4 ADU   ERROR
phi_opt twomass_psc 2MASS 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_POS-ANG
photZPCat MultiframeDetector WSA 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 WSACalib 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 WSATransit Photometric zero point for default extinction for the catalogue data {catalogue extension keyword:  MAGZPT} real 4 mags -0.9999995e9 ??
Derived detector zero-point in the sense of what magnitude object gives a total (corrected) flux of 1 count/s. These ZPs are appropriate for generating magnitudes in the natural detector+filter system based on Vega, see CASU reports for more details on colour equations etc. The ZPs have been derived from a robust average of all photometric standards observed on any particular set of frames, corrected for airmass but assuming the default extinction values listed later. For other airmass or other values of the extinction use
ZP → ZP - [sec(z)-1]×extinct + extinct default - extinct
You can then make use of any of the assorted flux estimators to produce magnitudes via
Mag = ZP - 2.5*log10(flux/exptime) - aperCor - skyCorr
Note that for the so-called total and isophotal flux options it is not possible to have a single-valued aperture correction.
photZPCat PreviousMFDZP WSA Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9  
photZPCat PreviousMFDZP WSACalib Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9  
photZPErrCat MultiframeDetector WSA Photometric zero point error for the catalogue data {catalogue extension keyword:  MAGZRR}
[Currently set to -1 for WFCAM data.]
real 4 mags -0.9999995e9 ??
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.
photZPErrCat MultiframeDetector WSACalib Photometric zero point error for the catalogue data {catalogue extension keyword:  MAGZRR}
[Currently set to -1 for WFCAM data.]
real 4 mags -0.9999995e9 ??
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.
photZPErrCat MultiframeDetector WSATransit Photometric zero point error for the catalogue data {catalogue extension keyword:  MAGZRR}
[Currently set to -1 for WFCAM data.]
real 4 mags -0.9999995e9 ??
Error in the zero point. If good photometric night this error will be at the level of a few percent. Values of 0.05 and above indicate correspondingly non-photometric night and worse.
photZPErrCat PreviousMFDZP WSA Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9  
photZPErrCat PreviousMFDZP WSACalib Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9  
pixelScale MultiframeDetector WSA Warning - Original detector pixel size, the actual angular pixel size is written to xPixSize and yPixSize in the CurrentAstrometry table {image extension keyword: PIXLSIZE} real 4 arcsec per pixel -0.9999995e9 INST_PIXSIZE
pixelScale MultiframeDetector WSACalib Warning - Original detector pixel size, the actual angular pixel size is written to xPixSize and yPixSize in the CurrentAstrometry table {image extension keyword: PIXLSIZE} real 4 arcsec per pixel -0.9999995e9 INST_PIXSIZE
pixelScale MultiframeDetector WSATransit Warning - Original detector pixel size, the actual angular pixel size is written to xPixSize and yPixSize in the CurrentAstrometry table {image extension keyword: PIXLSIZE} real 4 arcsec per pixel -0.9999995e9 INST_PIXSIZE
pmDec ukirtFSstars WSA Proper motion in Dec real 4 arcsec per year 0.0  
pmDec ukirtFSstars WSACalib Proper motion in Dec real 4 arcsec per year 0.0  
pmRA ukirtFSstars WSA Proper motion in RA real 4 arcsec per year 0.0  
pmRA ukirtFSstars WSACalib Proper motion in RA real 4 arcsec per year 0.0  
PN_1_BG twoxmm 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 XMM PN band 1 Maximum likelihood real 4      
PN_1_EXP twoxmm 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 XMM PN band 1 flux real 4 erg/cm**2/s    
PN_1_FLUX_ERR twoxmm XMM PN band 1 flux error real 4 erg/cm**2/s    
PN_1_RATE twoxmm XMM PN band 1 Count rates real 4 counts/s    
PN_1_RATE_ERR twoxmm XMM PN band 1 Count rates error real 4 counts/s    
PN_1_VIG twoxmm XMM The PN band 1 vignetting value. real 4      
PN_2_BG twoxmm 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 XMM PN band 2 Maximum likelihood real 4      
PN_2_EXP twoxmm 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 XMM PN band 2 flux real 4 erg/cm**2/s    
PN_2_FLUX_ERR twoxmm XMM PN band 2 flux error real 4 erg/cm**2/s    
PN_2_RATE twoxmm XMM PN band 2 Count rates real 4 counts/s    
PN_2_RATE_ERR twoxmm XMM PN band 2 Count rates error real 4 counts/s    
PN_2_VIG twoxmm XMM The PN band 2 vignetting value. real 4      
PN_3_BG twoxmm 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 XMM PN band 3 Maximum likelihood real 4      
PN_3_EXP twoxmm 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 XMM PN band 3 flux real 4 erg/cm**2/s    
PN_3_FLUX_ERR twoxmm XMM PN band 3 flux error real 4 erg/cm**2/s    
PN_3_RATE twoxmm XMM PN band 3 Count rates real 4 counts/s    
PN_3_RATE_ERR twoxmm XMM PN band 3 Count rates error real 4 counts/s    
PN_3_VIG twoxmm XMM The PN band 3 vignetting value. real 4      
PN_4_BG twoxmm 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 XMM PN band 4 Maximum likelihood real 4      
PN_4_EXP twoxmm 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 XMM PN band 4 flux real 4 erg/cm**2/s    
PN_4_FLUX_ERR twoxmm XMM PN band 4 flux error real 4 erg/cm**2/s    
PN_4_RATE twoxmm XMM PN band 4 Count rates real 4 counts/s    
PN_4_RATE_ERR twoxmm XMM PN band 4 Count rates error real 4 counts/s    
PN_4_VIG twoxmm XMM The PN band 4 vignetting value. real 4      
PN_5_BG twoxmm 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 XMM PN band 5 Maximum likelihood real 4      
PN_5_EXP twoxmm 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 XMM PN band 5 flux real 4 erg/cm**2/s    
PN_5_FLUX_ERR twoxmm XMM PN band 5 flux error real 4 erg/cm**2/s    
PN_5_RATE twoxmm XMM PN band 5 Count rates real 4 counts/s    
PN_5_RATE_ERR twoxmm XMM PN band 5 Count rates error real 4 counts/s    
PN_5_VIG twoxmm XMM The PN band 5 vignetting value. real 4      
PN_8_CTS twoxmm XMM Combined band source counts real 4 counts    
PN_8_CTS_ERR twoxmm XMM Combined band source counts 1 σ error real 4 counts    
PN_8_DET_ML twoxmm XMM PN band 8 Maximum likelihood real 4      
PN_8_FLUX twoxmm XMM PN band 8 flux real 4 erg/cm**2/s    
PN_8_FLUX_ERR twoxmm XMM PN band 8 flux error real 4 erg/cm**2/s    
PN_8_RATE twoxmm XMM PN band 8 Count rates real 4 counts/s    
PN_8_RATE_ERR twoxmm XMM PN band 8 Count rates error real 4 counts/s    
PN_9_DET_ML twoxmm XMM PN band 9 Maximum likelihood real 4      
PN_9_FLUX twoxmm XMM PN band 9 flux real 4 erg/cm**2/s    
PN_9_FLUX_ERR twoxmm XMM PN band 9 flux error real 4 erg/cm**2/s    
PN_9_RATE twoxmm XMM PN band 9 Count rates real 4 counts/s    
PN_9_RATE_ERR twoxmm XMM PN band 9 Count rates error real 4 counts/s    
PN_CHI2PROB twoxmm 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_FILTER twoxmm XMM PN filter. The options are Thick, Medium, Thin1, Thin2, and Open, depending on the efficiency of the optical blocking. varchar 6      
PN_FLAG twoxmm 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_HR1 twoxmm 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 XMM The 1 σ error of the PN hardness ratio between the bands 1 & 2 real 4      
PN_HR2 twoxmm 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 XMM The 1 σ error of the PN hardness ratio between the bands 2 & 3 real 4      
PN_HR3 twoxmm 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 XMM The 1 σ error of the PN hardness ratio between the bands 3 & 4 real 4      
PN_HR4 twoxmm 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 XMM The 1 σ error of the PN hardness ratio between the bands 4 & 5 real 4      
PN_MASKFRAC twoxmm 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 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 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 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      
pNearH iras_psc IRAS Number of nearby hours-confirmed point sources tinyint 1     NUMBER
pNearW iras_psc IRAS Number of nearby weeks-confirmed point sources tinyint 1     NUMBER
pNoise calSource, calSynopticSource WSACalib Probability that the source is noise real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

pNoise dxsJKsource, gcsPointSource, gcsZYJHKsource, gpsJHKsource, gpsPointSource, lasExtendedSource, lasPointSource, lasYJHKsource, reliableDxsSource, reliableGcsPointSource, reliableGpsPointSource, reliableLasPointSource, reliableUdsSource WSA Probability that the source is noise real 4     STAT_PROP
pNoise dxsSource, gcsSource, gpsSource, lasSource, udsSource WSA Probability that the source is noise real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

pointingID Multiframe WSA Pointing ID within survey {image primary HDU keyword: SURVEY_I} varchar 64   NONE ??
pointingID Multiframe WSACalib Pointing ID within survey {image primary HDU keyword: SURVEY_I} varchar 64   NONE ??
pointingID Multiframe WSATransit Pointing ID within survey {image primary HDU keyword: SURVEY_I} varchar 64   NONE ??
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_POS-ANG
posAng iras_psc IRAS Uncertainty ellipse position angle (East of North) smallint 2 degrees   POS_POS-ANG
posAngle CurrentAstrometry WSACalib orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 POS_POS-ANG
posAngle CurrentAstrometry WSATransit orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 POS_POS-ANG
posAngle CurrentAstrometry, PreviousAstrometry WSA orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 POS_POS-ANG
POSERR twoxmm 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    
ppErrBits dxsListRemeasurement, gcsListRemeasurement, gpsListRemeasurement, lasListRemeasurement, UKIDSSDetection, udsListRemeasurement WSA additional WFAU post-processing error bits int 4   0 CODE_MISC
ppErrBits calDetection WSACalib additional WFAU post-processing error bits int 4   0 CODE_MISC
Post-processing error quality bit flags assigned (NB: from UKIDSS DR2 release onwards) in the WSA 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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 LAS, GCS, GPS, DXS
0 6 Bad pixel(s) in default aperture 64 0x00000040 LAS, GCS, GPS, DXS
2 16 Close to saturated 65536 0x00010000 LAS, GCS, GPS
2 19 Possible crosstalk artefact/contamination 524288 0x00080000 LAS, GCS, DXS
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 LAS, GCS, GPS, DXS

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 K band sources in the LAS 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 calListRemeasurement WSACalib additional WFAU post-processing error bits int 4   0 CODE_MISC
ppErrBits dxsDetection, gcsDetection, gpsDetection, lasDetection, udsDetection WSA additional WFAU post-processing error bits int 4   0 CODE_MISC
Post-processing error quality bit flags assigned (NB: from UKIDSS DR2 release onwards) in the WSA 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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 LAS, GCS, GPS, DXS
0 6 Bad pixel(s) in default aperture 64 0x00000040 LAS, GCS, GPS, DXS
2 16 Close to saturated 65536 0x00010000 LAS, GCS, GPS
2 19 Possible crosstalk artefact/contamination 524288 0x00080000 LAS, GCS, DXS
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 LAS, GCS, GPS, DXS

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 K band sources in the LAS 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 ptsDetection WSATransit additional WFAU post-processing error bits int 4   0 CODE_MISC
Post-processing error quality bit flags assigned (NB: from UKIDSS DR2 release onwards) in the WSA 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:
ByteBitDetection quality issue Threshold or bit mask Applies to
DecimalHexadecimal
0 4 Deblended 16 0x00000010 LAS, GCS, GPS, DXS
0 6 Bad pixel(s) in default aperture 64 0x00000040 LAS, GCS, GPS, DXS
2 16 Close to saturated 65536 0x00010000 LAS, GCS, GPS
2 19 Possible crosstalk artefact/contamination 524288 0x00080000 LAS, GCS, DXS
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 LAS, GCS, GPS, DXS

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 K band sources in the LAS 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 WSA Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4   0  
ppErrBitsStatus ProgrammeFrame WSACalib Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4   0  
ppErrBitsStatus ProgrammeFrame WSATransit Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4   0  
priFlgLb rosat_bsc, rosat_fsc ROSAT priority flag L-broad tinyint 1     CODE_MISC
priFlgLh rosat_bsc, rosat_fsc ROSAT priority flag L-hard tinyint 1     CODE_MISC
priFlgLs rosat_bsc, rosat_fsc ROSAT priority flag L-soft tinyint 1     CODE_MISC
priFlgMb rosat_bsc, rosat_fsc ROSAT priority flag M-broad tinyint 1     CODE_MISC
priFlgMh rosat_bsc, rosat_fsc ROSAT priority flag M-hard tinyint 1     CODE_MISC
priFlgMs rosat_bsc, rosat_fsc ROSAT priority flag M-soft tinyint 1     CODE_MISC
priOrSec calSource WSACalib Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8   -99999999 CODE_MISC
Because of the spacing of the detectors in WFCAM, 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 calSourceRemeasurement WSACalib Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8     CODE_MISC
Because of the spacing of the detectors in WFCAM, 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 dxsJKsource, gcsPointSource, gcsZYJHKsource, gpsJHKsource, gpsPointSource, lasExtendedSource, lasPointSource, lasYJHKsource, reliableDxsSource, reliableGcsPointSource, reliableGpsPointSource, reliableLasPointSource, reliableUdsSource WSA Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8   -99999999 CODE_MISC
priOrSec dxsSource, gcsSource, gpsSource, lasSource, udsSource WSA Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates). bigint 8   -99999999 CODE_MISC
Because of the spacing of the detectors in WFCAM, 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 dxsSourceRemeasurement, gcsSourceRemeasurement, gpsSourceRemeasurement, lasSourceRemeasurement, udsSourceRemeasurement WSA Seam code for a unique (=0) or duplicated (!=0) source (eg. flags overlap duplicates) bigint 8     CODE_MISC
Because of the spacing of the detectors in WFCAM, 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 ProgrammeFrame WSA Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8   -99999999  
productID ProgrammeFrame WSACalib Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8   -99999999  
productID ProgrammeFrame WSATransit 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 WSA A unique identifier assigned to each required difference image product entry int 4     ??
productID RequiredDiffImage WSACalib A unique identifier assigned to each required difference image product entry int 4     ??
productID RequiredDiffImage WSATransit A unique identifier assigned to each required difference image product entry int 4     ??
productID RequiredMosaic WSA A unique identifier assigned to each required mosaic product entry int 4     ??
productID RequiredMosaic WSACalib A unique identifier assigned to each required mosaic product entry int 4     ??
productID RequiredMosaic WSATransit A unique identifier assigned to each required mosaic product entry int 4     ??
productID RequiredStack WSA A unique identifier assigned to each required stack product entry int 4     ??
productID RequiredStack WSACalib A unique identifier assigned to each required stack product entry int 4     ??
productID RequiredStack WSATransit A unique identifier assigned to each required stack product entry int 4     ??
programmeID Programme WSA UID of the archived programme coded as above int 4     ID_SURVEY
programmeID Programme WSACalib UID of the archived programme coded as above int 4     ID_SURVEY
programmeID Programme WSATransit UID of the archived programme coded as above int 4     ID_SURVEY
programmeID ProgrammeCurationHistory WSACalib the unique programme ID int 4     ID_SURVEY
programmeID ProgrammeCurationHistory WSATransit the unique programme ID int 4     ID_SURVEY
programmeID ProgrammeCurationHistory, ProgrammeTable, RequiredDiffImage, RequiredFilters, RequiredListDrivenProduct, RequiredMosaic, RequiredNeighbours, RequiredStack WSA the unique programme ID int 4     ID_SURVEY
programmeID ProgrammeFrame WSACalib WSA assigned programme UID {image primary HDU keyword: PROJECT} int 4   -99999999 ID_SURVEY
programmeID ProgrammeFrame WSATransit WSA assigned programme UID {image primary HDU keyword: PROJECT} int 4   -99999999 ID_SURVEY
programmeID ProgrammeFrame, SurveyProgrammes WSA WSA assigned programme UID {image primary HDU keyword: PROJECT} int 4   -99999999 ID_SURVEY
project Multiframe WSA Time-allocation code {image primary HDU keyword: PROJECT} varchar 64   NONE REFER_CODE
project Multiframe WSACalib Time-allocation code {image primary HDU keyword: PROJECT} varchar 64   NONE REFER_CODE
project Multiframe WSATransit Time-allocation code {image primary HDU keyword: PROJECT} varchar 64   NONE REFER_CODE
projection RequiredMosaic WSA CASU mosaic tool option to specify output WCS projection type (TAN for gnomonic, ZPN for zenithal polynomial) varchar 3     ??
projection RequiredMosaic WSACalib CASU mosaic tool option to specify output WCS projection type (TAN for gnomonic, ZPN for zenithal polynomial) varchar 3     ??
projection RequiredMosaic WSATransit CASU mosaic tool option to specify output WCS projection type (TAN for gnomonic, ZPN for zenithal polynomial) varchar 3     ??
propPeriod Programme WSA 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 WSACalib 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 WSATransit 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 WSA Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1     ??
proprietary Survey WSACalib Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1     ??
proprietary Survey WSATransit Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1     ??
prox twomass_psc, twomass_xsc 2MASS Proximity. real 4 arcsec   POS_ANG_DIST_GENERAL
pSaturated calSource, calSynopticSource WSACalib Probability that the source is saturated real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

pSaturated dxsJKsource, gcsPointSource, gcsZYJHKsource, gpsJHKsource, gpsPointSource, lasExtendedSource, lasPointSource, lasYJHKsource, reliableDxsSource, reliableGcsPointSource, reliableGpsPointSource, reliableLasPointSource, reliableUdsSource WSA Probability that the source is saturated real 4     STAT_PROP
pSaturated dxsSource, gcsSource, gpsSource, lasSource, udsSource WSA Probability that the source is saturated real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

psfFitChi2 UKIDSSDetection WSA standard normalised variance of PSF fit real 4   -0.9999995e9 FIT_STDEV
psfFitChi2 calDetection, calListRemeasurement WSACalib standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4   -0.9999995e9 FIT_STDEV
psfFitChi2 dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4   -0.9999995e9 FIT_STDEV
psfFitChi2 ptsDetection WSATransit standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4   -0.9999995e9 FIT_STDEV
psfFitChi2 udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_fit_chi2} real 4   -0.9999995e9  
psfFitDof UKIDSSDetection WSA no. of degrees of freedom of PSF fit smallint 2   -9999 STAT_N-DOF
psfFitDof calDetection, calListRemeasurement WSACalib no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2   -9999 STAT_N-DOF
psfFitDof dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2   -9999 STAT_N-DOF
psfFitDof ptsDetection WSATransit no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2   -9999 STAT_N-DOF
psfFitDof udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_fit_dof} smallint 2   -9999  
psfFitX UKIDSSDetection WSA PSF-fitted X coordinate real 4 pixels -0.9999995e9 POS_PLATE_X
psfFitX calDetection, calListRemeasurement WSACalib PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 POS_PLATE_X
psfFitX dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 POS_PLATE_X
psfFitX ptsDetection WSATransit PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 POS_PLATE_X
psfFitX udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_fit_X} real 4   -0.9999995e9  
psfFitXerr UKIDSSDetection WSA Error on PSF-fitted X coordinate real 4 pixels -0.9999995e9 ERROR
psfFitXerr calDetection, calListRemeasurement WSACalib Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 ERROR
psfFitXerr dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 ERROR
psfFitXerr ptsDetection WSATransit Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 ERROR
psfFitXerr udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_fit_X_err} real 4   -0.9999995e9  
psfFitY UKIDSSDetection WSA PSF-fitted Y coordinate real 4 pixels -0.9999995e9 POS_PLATE_Y
psfFitY calDetection, calListRemeasurement WSACalib PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 POS_PLATE_Y
psfFitY dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 POS_PLATE_Y
psfFitY ptsDetection WSATransit PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 POS_PLATE_Y
psfFitY udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_fit_Y} real 4   -0.9999995e9  
psfFitYerr UKIDSSDetection WSA Error on PSF-fitted Y coordinate real 4 pixels -0.9999995e9 ERROR
psfFitYerr calDetection, calListRemeasurement WSACalib Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 ERROR
psfFitYerr dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 ERROR
psfFitYerr ptsDetection WSATransit Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 ERROR
psfFitYerr udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_fit_y_err} real 4   -0.9999995e9  
psfFlux UKIDSSDetection WSA PSF-fitted flux real 4 ADU -0.9999995e9 PHOT_INTENSITY_ADU
psfFlux calDetection, calListRemeasurement WSACalib PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 PHOT_INTENSITY_ADU
psfFlux dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 PHOT_INTENSITY_ADU
psfFlux ptsDetection WSATransit PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 PHOT_INTENSITY_ADU
psfFlux udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_flux} real 4   -0.9999995e9  
psfFluxErr UKIDSSDetection WSA Error on PSF-fitted flux real 4 ADU -0.9999995e9 ERROR
psfFluxErr calDetection, calListRemeasurement WSACalib Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 ERROR
psfFluxErr dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, udsListRemeasurement WSA Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 ERROR
psfFluxErr ptsDetection WSATransit Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 ERROR
psfFluxErr udsDetection WSA Not available in SE output {catalogue TType keyword: PSF_flux_err} real 4   -0.9999995e9  
psfMag dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, UKIDSSDetection, udsListRemeasurement WSA PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE
psfMag calDetection, calListRemeasurement WSACalib PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE
psfMag ptsDetection WSATransit PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE
psfMag udsDetection WSA Not available in SE output real 4   -0.9999995e9  
psfMagErr dxsDetection, dxsListRemeasurement, gcsDetection, gcsListRemeasurement, gpsDetection, gpsListRemeasurement, lasDetection, lasListRemeasurement, UKIDSSDetection, udsListRemeasurement WSA Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 ERROR
psfMagErr calDetection, calListRemeasurement WSACalib Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 ERROR
psfMagErr ptsDetection WSATransit Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 ERROR
psfMagErr udsDetection WSA Not available in SE output real 4   -0.9999995e9  
pStar calSource, calSynopticSource WSACalib Probability that the source is a star real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

pStar dxsJKsource, gcsPointSource, gcsZYJHKsource, gpsJHKsource, gpsPointSource, lasExtendedSource, lasPointSource, lasYJHKsource, reliableDxsSource, reliableGcsPointSource, reliableGpsPointSource, reliableLasPointSource, reliableUdsSource WSA Probability that the source is a star real 4     STAT_PROP
pStar dxsSource, gcsSource, gpsSource, lasSource, udsSource WSA Probability that the source is a star real 4     STAT_PROP
Individual detection classifications are combined in the source merging process to produce a set of attributes for each merged source as follows. Presently, a basic classification table is defined that assigns reasonably accurate, self-consistent probability values for a given classification code:
FlagMeaning
Probability (%)
StarGalaxyNoiseSaturated
-9Saturated 0.0 0.0 5.095.0
-3Probable galaxy25.070.0 5.0 0.0
-2Probable star70.025.0 5.0 0.0
-1Star90.0 5.0 5.0 0.0
0Noise 5.0 5.090.0 0.0
+1Galaxy 5.090.0 5.0 0.0

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

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

pts_key twomass_psc 2MASS A unique identification number for the PSC source. int 4     ID_NUMBER
pts_key twomass_xsc 2MASS key to point source data DB record. int 4     ID_NUMBER
pv21 CurrentAstrometry WSACalib Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv21 CurrentAstrometry WSATransit Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv21 CurrentAstrometry, PreviousAstrometry WSA Coefficient for r term (use only with ZPN projection) {image extension keyword: PV2_1}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv22 CurrentAstrometry WSACalib Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv22 CurrentAstrometry WSATransit Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv22 CurrentAstrometry, PreviousAstrometry WSA Coefficient for r**2 term (use only with ZPN projection) {image extension keyword: PV2_2}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv23 CurrentAstrometry WSACalib Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv23 CurrentAstrometry WSATransit Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pv23 CurrentAstrometry, PreviousAstrometry WSA Coefficient for r**3 term (use only with ZPN projection) {image extension keyword: PV2_3}
transformation from pixel to celestial co-ordinates
float 8   -0.9999995e9 POS_TRANSF_PARAM
pxcntr twomass_psc 2MASS The pts_key value of the nearest source in the PSC. int 4     NUMBER
pxcntr twomass_xsc 2MASS ext_key value of nearest XSC source. int 4     NUMBER
pxpa twomass_psc, twomass_xsc 2MASS 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_POS-ANG



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23/02/2010