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Glossary of WSA NonSurvey attributes (UKIDSSDR10)

This Glossary alphabetically lists all attributes used in the WSA NonSurvey database(s) held in the WSA. If you would like to have more information about the schema tables please use the 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 [nspid]Detection WSA NonSurvey 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
pairingCriterion [nspid]Programme WSA NonSurvey The pairing criterion for associating detections into merged sources real 4 Degrees   ??
parallax [nspid]Variability WSA NonSurvey 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 [nspid]MultiframeDetector WSA NonSurvey PC system identifier {image extension keyword: PCSYSID} varchar 32   NONE ??
petroFlux [nspid]Detection WSA NonSurvey flux within circular aperture to k × r_p ; k = 2 {catalogue TType keyword: Petr_flux} real 4 ADU   PHOT_INTENSITY_ADU
petroFluxErr [nspid]Detection WSA NonSurvey error on Petrosian flux {catalogue TType keyword: Petr_flux_err} real 4 ADU   ERROR
petroMag [nspid]Detection WSA NonSurvey Calibrated Petrosian magnitude within circular aperture r_p real 4 mag   PHOT_INT-MAG
petroMagErr [nspid]Detection WSA NonSurvey error on calibrated Petrosian magnitude real 4 mag   ERROR
petroRad [nspid]Detection WSA NonSurvey r_p as defined in Yasuda et al. 2001 AJ 112 1104 {catalogue TType keyword: Petr_radius} real 4 pixels   EXTENSION_RAD
pGalaxy [nspid]Source, [nspid]SynopticSource WSA NonSurvey 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.

pHeight [nspid]Detection WSA NonSurvey 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
pHeightErr [nspid]Detection WSA NonSurvey Error in peak height {catalogue TType keyword: Peak_height_err} real 4 ADU   ERROR
photZPCat [nspid]MultiframeDetector WSA NonSurvey 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 [nspid]PreviousMFDZP WSA NonSurvey Photometric zeropoint for default extinction in catalogue header real 4 mag -0.9999995e9  
photZPErrCat [nspid]MultiframeDetector WSA NonSurvey 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 [nspid]PreviousMFDZP WSA NonSurvey Photometric zeropoint error in catalogue header real 4 mag -0.9999995e9  
pixelScale [nspid]MultiframeDetector WSA NonSurvey 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 phys.angSize;instr.pixel
pixelSize [nspid]RequiredMosaic WSA NonSurvey The final pixel size of the mosaic real 4 arcsec -0.9999995e9 ??
pmDec [nspid]FSstars WSA NonSurvey Proper motion in Dec real 4 arcsec per year 0.0  
pmRA [nspid]FSstars WSA NonSurvey Proper motion in RA real 4 arcsec per year 0.0  
pNoise [nspid]Source, [nspid]SynopticSource WSA NonSurvey 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 [nspid]Multiframe WSA NonSurvey Pointing ID within survey {image primary HDU keyword: SURVEY_I} varchar 64   NONE ??
posAngle [nspid]CurrentAstrometry, [nspid]PreviousAstrometry WSA NonSurvey orientation of image x-axis to N-S float 8 Degrees -0.9999995e9 pos.posAng
ppErrBits [nspid]Detection WSA NonSurvey 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 All VDFS catalogues
0 6 Bad pixel(s) in default aperture 64 0x00000040 All VDFS catalogues
1 15 Source in poor flat field region 32768 0x00008000 All but mosaics
2 16 Close to saturated 65536 0x00010000 All VDFS catalogues (though deeps excluded prior to DR8)
2 17 Photometric calibration probably subject to systematic error 131072 0x00020000 GPS only
2 19 Possible crosstalk artefact/contamination 524288 0x00080000 All but GPS
2 22 Lies within a dither offset of the stacked frame boundary 4194304 0x00400000 All but mosaics

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 [nspid]Orphan WSA NonSurvey additional WFAU post-processing error bits int 4   0 CODE_MISC
ppErrBitsStatus [nspid]ProgrammeFrame WSA NonSurvey Bit flag to denote whether detection quality flagging has been done on this multiframe for this programme. int 4   0  
priOrSec [nspid]Source WSA NonSurvey 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).
productID [nspid]ProgrammeFrame WSA NonSurvey Product ID of deep stack frame (or intermediate stack if used as a deep stack). {image primary HDU keyword: PRODID} bigint 8   -99999999  
productID [nspid]RequiredDiffImage WSA NonSurvey A unique identifier assigned to each required difference image product entry int 4     ??
productID [nspid]RequiredMosaic WSA NonSurvey A unique identifier assigned to each required mosaic product entry int 4     ??
productID [nspid]RequiredStack WSA NonSurvey A unique identifier assigned to each required stack product entry int 4     ??
programmeID [nspid]ProductLinks, [nspid]ProgrammeCurationHistory, [nspid]ProgrammeTable, [nspid]RequiredDiffImage, [nspid]RequiredFilters, [nspid]RequiredListDrivenProduct, [nspid]RequiredMosaic, [nspid]RequiredNeighbours, [nspid]RequiredStack WSA NonSurvey the unique programme ID int 4     ID_SURVEY
programmeID [nspid]Programme WSA NonSurvey UID of the archived programme coded as above int 4     ID_SURVEY
programmeID [nspid]ProgrammeFrame WSA NonSurvey WSA assigned programme UID {image primary HDU keyword: PROJECT} int 4   -99999999 meta.id
programmeID [nspid]SurveyProgrammes WSA NonSurvey WSA assigned programme UID {image primary HDU keyword: PROJECT} int 4   -99999999 ID_SURVEY
project [nspid]Multiframe WSA NonSurvey Time-allocation code {image primary HDU keyword: PROJECT} varchar 64   NONE meta.bib
propPeriod [nspid]Programme WSA NonSurvey the proprietory period for any data taken for this programme in months, e.g. 12 for open time. int 4 months   TIME_PERIOD
proprietary [nspid]Survey WSA NonSurvey Logical flag indicating whether a survey is proprietary or not (1=yes; 0=no) tinyint 1     ??
pSaturated [nspid]Source, [nspid]SynopticSource WSA NonSurvey 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 [nspid]Detection WSA NonSurvey standard normalised variance of PSF fit {catalogue TType keyword: PSF_fit_chi2} real 4   -0.9999995e9 FIT_STDEV
psfFitDof [nspid]Detection WSA NonSurvey no. of degrees of freedom of PSF fit {catalogue TType keyword: PSF_fit_dof} smallint 2   -9999 STAT_N-DOF
psfFitX [nspid]Detection WSA NonSurvey PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X} real 4 pixels -0.9999995e9 POS_PLATE_X
psfFitXerr [nspid]Detection WSA NonSurvey Error on PSF-fitted X coordinate {catalogue TType keyword: PSF_fit_X_err} real 4 pixels -0.9999995e9 ERROR
psfFitY [nspid]Detection WSA NonSurvey PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_Y} real 4 pixels -0.9999995e9 POS_PLATE_Y
psfFitYerr [nspid]Detection WSA NonSurvey Error on PSF-fitted Y coordinate {catalogue TType keyword: PSF_fit_y_err} real 4 pixels -0.9999995e9 ERROR
psfFlux [nspid]Detection WSA NonSurvey PSF-fitted flux {catalogue TType keyword: PSF_flux} real 4 ADU -0.9999995e9 PHOT_INTENSITY_ADU
psfFluxErr [nspid]Detection WSA NonSurvey Error on PSF-fitted flux {catalogue TType keyword: PSF_flux_err} real 4 ADU -0.9999995e9 ERROR
psfMag [nspid]Detection WSA NonSurvey PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 PHOT_PROFILE
psfMagErr [nspid]Detection WSA NonSurvey Error on PSF-fitted calibrated magnitude real 4 mag -0.9999995e9 ERROR
pStar [nspid]Source, [nspid]SynopticSource WSA NonSurvey 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.

pv21 [nspid]CurrentAstrometry, [nspid]PreviousAstrometry WSA NonSurvey 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 stat.fit.param
pv22 [nspid]CurrentAstrometry, [nspid]PreviousAstrometry WSA NonSurvey 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 stat.fit.param
pv23 [nspid]CurrentAstrometry, [nspid]PreviousAstrometry WSA NonSurvey 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 stat.fit.param



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07/11/2017