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### Glossary of WSA NonSurvey attributes (UKIDSSDR11)

##### 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

### F

NameSchema TableDatabaseDescriptionTypeLengthUnitDefault ValueUnified Content Descriptor
fastGuiderMode [nspid]Multiframe WSA NonSurvey Fast guider mode {image primary HDU keyword: FGMODE} varchar 32   NONE
feiiAperMag3 [nspid]Source WSA NonSurvey Default point source Feii aperture corrected mag (2.0 arcsec aperture diameter)
If in doubt use this flux estimator
real 4 mag -0.9999995e9 PHOT_MAG
feiiAperMag3Err [nspid]Source WSA NonSurvey Error in default point source Feii mag (2.0 arcsec aperture diameter) real 4 mag -0.9999995e9 ERROR
feiiAperMag4 [nspid]Source WSA NonSurvey Point source Feii aperture corrected mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 PHOT_MAG
feiiAperMag4Err [nspid]Source WSA NonSurvey Error in point source Feii mag (2.8 arcsec aperture diameter) real 4 mag -0.9999995e9 ERROR
feiiAperMag6 [nspid]Source WSA NonSurvey Point source Feii aperture corrected mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 PHOT_MAG
feiiAperMag6Err [nspid]Source WSA NonSurvey Error in point source Feii mag (5.7 arcsec aperture diameter) real 4 mag -0.9999995e9 ERROR
feiiaStratAst [nspid]VarFrameSetInfo WSA NonSurvey Strateva parameter, a, in fit to astrometric rms vs magnitude in Feii band, see Sesar et al. 2007. real 4   -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiiaStratAst [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiiaStratPht [nspid]VarFrameSetInfo WSA NonSurvey Strateva parameter, a, in fit to photometric rms vs magnitude in Feii band, see Sesar et al. 2007. real 4   -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiiaStratPht [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiibestAper [nspid]Variability WSA NonSurvey Best aperture (1-6) for photometric statistics in the Feii band int 4   -9999
Aperture magnitude (1-6) which gives the lowest RMS for the object. All apertures have the appropriate aperture correction. This can give better values in crowded regions than aperMag3 (see Irwin et al. 2007, MNRAS, 375, 1449)
feiibStratAst [nspid]VarFrameSetInfo WSA NonSurvey Strateva parameter, b, in fit to astrometric rms vs magnitude in Feii band, see Sesar et al. 2007. real 4   -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiibStratAst [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiibStratPht [nspid]VarFrameSetInfo WSA NonSurvey Strateva parameter, b, in fit to photometric rms vs magnitude in Feii band, see Sesar et al. 2007. real 4   -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiibStratPht [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c1 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiichiSqAst [nspid]VarFrameSetInfo WSA NonSurvey Goodness of fit of Strateva function to astrometric data in Feii band real 4   -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiichiSqAst [nspid]VarFrameSetInfo WSA NonSurvey Goodness of fit of Strateva function to astrometric data in Feii band real 4   -0.9999995e9 stat.fit.goodness
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiichiSqpd [nspid]Variability WSA NonSurvey Chi square (per degree of freedom) fit to data (mean and expected rms) real 4   -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiichiSqPht [nspid]VarFrameSetInfo WSA NonSurvey Goodness of fit of Strateva function to photometric data in Feii band real 4   -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiiClass [nspid]Source WSA NonSurvey discrete image classification flag in Feii smallint 2   -9999 CLASS_MISC
feiiClassStat [nspid]Source WSA NonSurvey N(0,1) stellarness-of-profile statistic in Feii real 4   -0.9999995e9 STAT_PROP
feiicStratAst [nspid]VarFrameSetInfo WSA NonSurvey Strateva parameter, c, in fit to astrometric rms vs magnitude in Feii band, see Sesar et al. 2007. real 4   -0.9999995e9
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiicStratAst [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiicStratPht [nspid]VarFrameSetInfo WSA NonSurvey Strateva parameter, c, in fit to photometric rms vs magnitude in Feii band, see Sesar et al. 2007. real 4   -0.9999995e9
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiicStratPht [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c2 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiiDeblend [nspid]Source WSA NonSurvey placeholder flag indicating parent/child relation in Feii int 4   -99999999 CODE_MISC
This CASU pipeline processing source extraction flag is a placeholder only, and is always set to zero in all passbands in the merged source lists. If you need to know when a particular image detection is a component of a deblend or not, test bit 4 of attribute ppErrBits (see corresponding glossary entry) which is set by WFAU's post-processing software based on testing the areal profiles aprof2-8 (these are set by CASU to -1 for deblended components, or positive values for non-deblended detections). We encode this in an information bit of ppErrBits for convenience when querying the merged source tables.
feiidStratAst [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c3 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to astrometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiidStratPht [nspid]VarFrameSetInfo WSA NonSurvey Parameter, c0 from Ferreira-Lopes & Cross 2017, Eq. 18, in fit to photometric rms vs magnitude in Feii band. real 4   -0.9999995e9 stat.fit.param
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiiEll [nspid]Source WSA NonSurvey 1-b/a, where a/b=semi-major/minor axes in Feii real 4   -0.9999995e9 PHYS_ELLIPTICITY
feiieNum [nspid]MergeLog WSA NonSurvey the extension number of this Feii frame tinyint 1     NUMBER
feiiErrBits [nspid]Source WSA NonSurvey processing warning/error bitwise flags in Feii int 4   -99999999 CODE_MISC
Apparently not actually an error bit flag, but a count of the number of zero confidence pixels in the default (2 arcsec diameter) aperture.
feiiEta [nspid]Source WSA NonSurvey Offset of Feii detection from master position (+north/-south) real 4 arcsec -0.9999995e9 POS_EQ_DEC_OFF
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 2.0 (UKIDSS LAS and GPS; UHS; also non-survey programmes) or 1.0 (UKIDSS GPS, DXS and UDS) arcseconds is used, the higher value enabling pairing of moving sources when epoch separations may be several years. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the LAS, you might wish to insist that the offsets in the selected sample are all below 1 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
feiiexpML [nspid]VarFrameSetInfo WSA NonSurvey Expected magnitude limit of frameSet in this in Feii band. real 4   -0.9999995e9
The expected magnitude limit of an intermediate stack, based on the total exposure time. expML=Filter.oneSecML+1.25*log10(totalExpTime). Since different intermediate stacks can have different exposure times, the totalExpTime is the minimum, as long as the number of stacks with this minimum make up 10% of the total. This is a more conservative treatment than just taking the mean or median total exposure time.
feiiExpRms [nspid]Variability WSA NonSurvey Rms calculated from polynomial fit to modal RMS as a function of magnitude in Feii band real 4 mag -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiiGausig [nspid]Source WSA NonSurvey RMS of axes of ellipse fit in Feii real 4 pixels -0.9999995e9 MORPH_PARAM
feiiHallMag [nspid]Source WSA NonSurvey Total point source Feii mag real 4 mag -0.9999995e9 PHOT_MAG
feiiHallMagErr [nspid]Source WSA NonSurvey Error in total point source Feii mag real 4 mag -0.9999995e9 ERROR
feiiIntRms [nspid]Variability WSA NonSurvey Intrinsic rms in Feii-band real 4 mag -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiiisDefAst [nspid]VarFrameSetInfo WSA NonSurvey Use a default model for the astrometric noise in Feii band. tinyint 1   0
feiiisDefAst [nspid]VarFrameSetInfo WSA NonSurvey Use a default model for the astrometric noise in Feii band. tinyint 1   0 meta.code
feiiisDefPht [nspid]VarFrameSetInfo WSA NonSurvey Use a default model for the photometric noise in Feii band. tinyint 1   0
feiiMagMAD [nspid]Variability WSA NonSurvey Median Absolute Deviation of Feii magnitude real 4 mag -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiiMagRms [nspid]Variability WSA NonSurvey rms of Feii magnitude real 4 mag -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiimaxCadence [nspid]Variability WSA NonSurvey maximum gap between observations real 4 days -0.9999995e9
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiiMaxMag [nspid]Variability WSA NonSurvey Maximum magnitude in Feii band, of good detections real 4   -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiimeanMag [nspid]Variability WSA NonSurvey Mean Feii magnitude real 4 mag -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiimedCadence [nspid]Variability WSA NonSurvey median gap between observations real 4 days -0.9999995e9
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiimedianMag [nspid]Variability WSA NonSurvey Median Feii magnitude real 4 mag -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiimfID [nspid]MergeLog WSA NonSurvey the UID of the relevant Feii multiframe bigint 8     ID_FRAME
feiiminCadence [nspid]Variability WSA NonSurvey minimum gap between observations real 4 days -0.9999995e9
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiiMinMag [nspid]Variability WSA NonSurvey Minimum magnitude in Feii band, of good detections real 4   -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiindof [nspid]Variability WSA NonSurvey Number of degrees of freedom for chisquare smallint 2   -9999
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiinDofAst [nspid]VarFrameSetInfo WSA NonSurvey Number of degrees of freedom of astrometric fit in Feii band. smallint 2   -9999
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiinDofAst [nspid]VarFrameSetInfo WSA NonSurvey Number of degrees of freedom of astrometric fit in Feii band. smallint 2   -9999 stat.fit.dof;stat.param
The best fit solution to the expected RMS position around the mean for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated.
feiinDofPht [nspid]VarFrameSetInfo WSA NonSurvey Number of degrees of freedom of photometric fit in Feii band. smallint 2   -9999
The best fit solution to the expected RMS brightness (in magnitudes) for all objects in the frameset. Objects were binned in ranges of magnitude and the median RMS (after clipping out variable objects using the median-absolute deviation) was calculated. The Strateva function $\zeta(m)>=a+b\,10^{0.4m}+c\,10^{0.8m}$ was fit, where $\zeta(m)$ is the expected RMS as a function of magnitude. The chi-squared and number of degrees of freedom are also calculated. This technique was used in Sesar et al. 2007, AJ, 134, 2236.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u12akasi1Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u12bkasi1Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u13akasi1Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u13akasi2Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u13akasi3Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u17auo01Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u18beap003Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinFlaggedObs [nspid]Variability WSA NonSurvey Number of detections in Feii band flagged as potentially spurious by u18bnav04Detection.ppErrBits int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiinGoodObs [nspid]Variability WSA NonSurvey Number of good detections in Feii band int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiiNgt3sig [nspid]Variability WSA NonSurvey Number of good detections in Feii-band that are more than 3 sigma deviations (feiiAperMagN < (feiiMeanMag-3*feiiMagRms) smallint 2   -9999
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiinMissingObs [nspid]Variability WSA NonSurvey Number of Feii band frames that this object should have been detected on and was not int 4   0
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiiPA [nspid]Source WSA NonSurvey ellipse fit celestial orientation in Feii real 4 Degrees -0.9999995e9 POS_POS-ANG
feiiPetroMag [nspid]Source WSA NonSurvey Extended source Feii mag (Petrosian) real 4 mag -0.9999995e9 PHOT_MAG
feiiPetroMagErr [nspid]Source WSA NonSurvey Error in extended source Feii mag (Petrosian) real 4 mag -0.9999995e9 ERROR
feiippErrBits [nspid]Source WSA NonSurvey additional WFAU post-processing error bits in Feii 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:
 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 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.
feiiprobVar [nspid]Variability WSA NonSurvey Probability of variable from chi-square (and other data) real 4   -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiiPsfMag [nspid]Source WSA NonSurvey Point source profile-fitted Feii mag real 4 mag -0.9999995e9 PHOT_MAG
feiiPsfMagErr [nspid]Source WSA NonSurvey Error in point source profile-fitted Feii mag real 4 mag -0.9999995e9 ERROR
feiiSeqNum [nspid]Source WSA NonSurvey the running number of the Feii detection int 4   -99999999 ID_NUMBER
feiiSerMag2D [nspid]Source WSA NonSurvey Extended source Feii mag (profile-fitted) real 4 mag -0.9999995e9 PHOT_MAG
feiiSerMag2DErr [nspid]Source WSA NonSurvey Error in extended source Feii mag (profile-fitted) real 4 mag -0.9999995e9 ERROR
feiiskewness [nspid]Variability WSA NonSurvey Skewness in Feii band (see Sesar et al. 2007) real 4   -0.9999995e9
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiitotalPeriod [nspid]Variability WSA NonSurvey total period of observations (last obs-first obs) real 4 days -0.9999995e9
The observations are classified as good, flagged or missing. Flagged observations are ones where the object has a ppErrBit flag. Missing observations are observations of the part of the sky that include the position of the object, but had no detection. All the statistics are calculated from good observations. The cadence parameters give the minimum, median and maximum time between observations, which is useful to know if the data could be used to find a particular type of variable.
feiiVarClass [nspid]Variability WSA NonSurvey Classification of variability in this band smallint 2   -9999
The photometry is calculated for good observations in the best aperture. The mean, rms, median, median absolute deviation, minMag and maxMag are quite standard. The skewness is calculated as in Sesar et al. 2007, AJ, 134, 2236. The number of good detections that are more than 3 standard deviations can indicate a distribution with many outliers. In each frameset, the mean and rms are used to derive a fit to the expected rms as a function of magnitude. The parameters for the fit are stored in VarFrameSetInfo and the value for the source is in expRms. This is subtracted from the rms in quadrature to get the intrinsic rms: the variability of the object beyond the noise in the system. The chi-squared is calculated, assuming a non-variable object which has the noise from the expected-rms and mean calculated as above. The probVar statistic assumes a chi-squared distribution with the correct number of degrees of freedom. The varClass statistic is 1, if the probVar>0.9 and intrinsicRMS/expectedRMS>3.
feiiXi [nspid]Source WSA NonSurvey Offset of Feii detection from master position (+east/-west) real 4 arcsec -0.9999995e9 POS_EQ_RA_OFF
When associating individual passband detections into merged sources, a generous (in terms of the positional uncertainties) pairing radius of 2.0 (UKIDSS LAS and GPS; UHS; also non-survey programmes) or 1.0 (UKIDSS GPS, DXS and UDS) arcseconds is used, the higher value enabling pairing of moving sources when epoch separations may be several years. Such a large association criterion can of course lead to spurious pairings in the merged sources lists (although note that between passband pairs, handshake pairing is done: both passbands must agree that the candidate pair is their nearest neighbour for the pair to propagate through into the merged source table). In order to help filter spurious pairings out, and assuming that large positional offsets between the different passband detections are not expected (e.g. because of source motion, or larger than usual positional uncertainties) then the attributes Xi and Eta can be used to filter any pairings with suspiciously large offsets in one or more bands. For example, for a clean sample of QSOs from the LAS, you might wish to insist that the offsets in the selected sample are all below 1 arcsecond: simply add WHERE clauses into the SQL sample selection script to exclude all Xi and Eta values larger than the threshold you want. NB: the master position is the position of the detection in the shortest passband in the set, rather than the ra/dec of the source as stored in source attributes of the same name. The former is used in the pairing process, while the latter is generally the optimally weighted mean position from an astrometric solution or other combinatorial process of all individual detection positions across the available passbands.
fieldID [nspid]ProgrammeFrame WSA NonSurvey UID of position on sky, set just after ProgrammeBuilder runs int 4   -99999999 meta.bib
fieldID [nspid]QsoMapVarFrameSetInfo WSA NonSurvey fieldID, assigned in grouping procedure bigint 8     meta.id;obs.field
fieldID [nspid]QsoMapVariability WSA NonSurvey field ID assigned by ProgrammeBuilder module when grouping. bigint 8     meta.id;obs.field
fieldID [nspid]RequiredMergeLogMultiEpoch WSA NonSurvey UID of position on sky. References Required{productType}.fieldID int 4   -99999999 ??
fieldID [nspid]RequiredMosaic, [nspid]RequiredStack WSA NonSurvey UID of position on sky. int 4   -99999999 ??
fieldID [nspid]StdFieldInfo WSA NonSurvey The fieldID is a UID identifying each of the 43 standard fields that are observed as part of the calibration observations int 4     obs.field
fieldName [nspid]StdFieldInfo WSA NonSurvey reference name of field varchar 16   NONE ????
fileName [nspid]Multiframe WSA NonSurvey the filename for the multiframe, eg. server:/path/filename.fit varchar 256     meta.id;meta.file
fileNameRoot [nspid]ExternalProduct WSA NonSurvey File name root of the products varchar 32
fileTimeStamp [nspid]Multiframe WSA NonSurvey Time stamp digits (from the original CASU directory name and file time stamp) for enforcing uniqueness bigint 8     ??
filter1 [nspid]RequiredDiffImage WSA NonSurvey UID of WFCAM narrow band (primary) filter tinyint 1     meta.code;instr.filter
filter2 [nspid]RequiredDiffImage WSA NonSurvey UID of WFCAM broad band (reference) filter to be subtracted tinyint 1     meta.code;instr.filter
filteredImageName [nspid]MapFrameStatus WSA NonSurvey the filename of the associated filtered image name, eg. server:/path/filename_st_tl_two.fit varchar 256   NONE
filterID [nspid]Detection, [nspid]SatelliteDetection, [nspid]UKIDSSDetection WSA NonSurvey UID of combined filter (assigned in WSA: 1=Z,2=Y,3=J,4=H,5=K,6=H2,7=Br,8=blank) tinyint 1     INST_FILTER_CODE
filterID [nspid]Filter, [nspid]RequiredFilters, [nspid]RequiredMosaic, [nspid]RequiredStack WSA NonSurvey UID of combined filter (assigned in WSA: 1=Z,2=Y,3=J,4=H,5=K,6=H2,7=Br,8=blank) tinyint 1     meta.code;instr.filter
filterID [nspid]MapRemeasAver, [nspid]MapRemeasurement WSA NonSurvey UID of combined filter (assigned in VSA: 1=Z,2=Y,3=J,4=H,5=K,6=H2,7=Br,8=blank) tinyint 1     meta.code;instr.filter
filterID [nspid]Multiframe WSA NonSurvey UID of combined filter (assigned in WSA: 1=Z,2=Y,3=J,4=H,5=K,6=H2,7=Br,8=blank,9=1.205nbJ,10=1.619nbH,11=1.644FeII) tinyint 1     meta.code;instr.filter
filterID [nspid]MultiframeDetector WSA NonSurvey UID of combined filter (assigned in WSA: 1=Z,2=Y,3=J,4=H,5=K,6=H2,7=Br,8=blank,9=1.205nbJ,10=1.619nbH,11=1.644FeII) {image primary HDU keyword: FILTER} tinyint 1     meta.code;instr.filter
filterID [nspid]Orphan WSA NonSurvey UID of combined filter tinyint 1     INST_FILTER_CODE
filterID [nspid]RequiredMergeLogMultiEpoch WSA NonSurvey UID of combined filter (assigned in OSA: 1=u,2=g,3=r,4=i,5=z,6=blank) tinyint 1     meta.code;instr.filter
filterName [nspid]Multiframe WSA NonSurvey WFCAM combined filter name {image primary HDU keyword: FILTER} varchar 8     ??
filterType [nspid]Filter WSA NonSurvey The type of filter BROAD, NARROW, BROADLIST varchar 16   NONE
finalProductTable [nspid]RequiredMatchedApertureProduct WSA NonSurvey the name of the final product table for this product varchar 64     ID_TABLE
firstDerMM [nspid]SatelliteOrbits WSA NonSurvey First time derviative of the Mean Motion divided by two float 8
flag [nspid]SourceXDetectionBestMatch, [nspid]SourceXSynopticSourceBestMatch WSA NonSurvey Flag for potential matching problems tinyint 1   0
flag=1 if the same intermediate stack detection is linked to two different unique sources. This can happen in images where the seeing was poorer than average or if a source has moved over time and overlaps with another source. flag=2 no intermediate stack detection, but the expected location is in 1 dither offset of the edge of the stack.
flatID [nspid]Multiframe WSA NonSurvey UID of library calibration flatfield frame {image extension keyword: FLATCOR} bigint 8   -99999999 obs.field
flux [nspid]SatelliteDetection WSA NonSurvey Instrumental isophotal flux counts real 4 ADU   PHOT_INTENSITY_ADU
fluxErr [nspid]SatelliteDetection WSA NonSurvey Error in instrumental isophotal flux counts real 4 ADU   ERROR
focusFiltOff [nspid]Multiframe WSA NonSurvey Focus filter offset {image primary HDU keyword: FOC_FOFF} real 4 millimetres -0.9999995e9 instr.param
focusInstFiltOff [nspid]Multiframe WSA NonSurvey focus offset for inst. filter {image primary HDU keyword: TEL_FOFF} real 4 millimetres -0.9999995e9 instr.param
focusNominOff [nspid]Multiframe WSA NonSurvey Offset from nominal focus position {image primary HDU keyword: FOC_OFF} real 4 millimetres -0.9999995e9 instr.param
focusOffset [nspid]Multiframe WSA NonSurvey Focus offset {image primary HDU keyword: FOC_OFFS} real 4 millimetres -0.9999995e9 instr.param
focusPos [nspid]Multiframe WSA NonSurvey Focus position {image primary HDU keyword: FOC_POSN} real 4 millimetres -0.9999995e9 instr.param
focusSerial [nspid]Multiframe WSA NonSurvey Serial number in focus scan {image primary HDU keyword: FOC_I} int 4   -99999999 ??
focusZero [nspid]Multiframe WSA NonSurvey Focus zero-point position {image primary HDU keyword: FOC_ZERO} real 4 millimetres -0.9999995e9 instr.param
frameSetID [nspid]DxsSource, [nspid]ExtendedSource, [nspid]GcsPointSource, [nspid]GpsPointSource, [nspid]JHKsource, [nspid]JKsource, [nspid]LasPointSource, [nspid]PointSource, [nspid]UdsSource, [nspid]YJHKsource, [nspid]ZYJHKsource WSA NonSurvey UID of the set of frames that this merged source comes from bigint 8     REFER_CODE
frameSetID [nspid]FrameSets, [nspid]JHKmergeLog, [nspid]JKmergeLog, [nspid]YJHKmergeLog, [nspid]ZYJHKmergeLog WSA NonSurvey frame set ID, unique over the whole WSA via programme ID prefix, assigned by merging procedure bigint 8     ID_FIELD
frameSetID [nspid]MergeLog, [nspid]VarFrameSetInfo, [nspid]Variability WSA NonSurvey frame set ID, unique over the whole WSA via programme ID prefix, assigned by merging procedure bigint 8     ID_FIELD
Each merged source in the merged source tables come from a set of individual passband frames (with different filters and/or different epochs of observation). In the WSA, a frame is generally the image provided by one detector (dither-stacked and interlaced as appropriate); hence a frame set comprises a set of individual detector frames in different passbands and/or at different observation epochs. Each frame set is uniquely identified by the attribute frameSetID, and this references a row in the corresponding merge log for the source table (for example, lasSource.frameSetID references lasMergeLog.frameSetID. The merge log in turn references the full set of image descriptive data held in the tables MultiframeDetector and ultimately Multiframe (these two tables map directly onto the multi-extension FITS file hierarchy of extension FITS headers beneath a single primary HDU FITS header - primary HDU FITS keys will be found in Multiframe, while the corresponding extension FITS keys for each primary set will be found in table MultiframeDetector). In this way, you can trace the provenance of a merged source record right back to the individual image frames from which it is derived.
frameSetID [nspid]Source WSA NonSurvey UID of the set of frames that this merged source comes from bigint 8     REFER_CODE
Each merged source in the merged source tables come from a set of individual passband frames (with different filters and/or different epochs of observation). In the WSA, a frame is generally the image provided by one detector (dither-stacked and interlaced as appropriate); hence a frame set comprises a set of individual detector frames in different passbands and/or at different observation epochs. Each frame set is uniquely identified by the attribute frameSetID, and this references a row in the corresponding merge log for the source table (for example, lasSource.frameSetID references lasMergeLog.frameSetID. The merge log in turn references the full set of image descriptive data held in the tables MultiframeDetector and ultimately Multiframe (these two tables map directly onto the multi-extension FITS file hierarchy of extension FITS headers beneath a single primary HDU FITS header - primary HDU FITS keys will be found in Multiframe, while the corresponding extension FITS keys for each primary set will be found in table MultiframeDetector). In this way, you can trace the provenance of a merged source record right back to the individual image frames from which it is derived.
frameSetTolerance [nspid]Programme WSA NonSurvey The match tolerance for different passband frames real 4 Degrees   ??
frameType [nspid]Multiframe WSA NonSurvey The type of multiframe (eg. stack|tile|mosaic|difference|calibration|interleaved etc).
A multiframe can have a combination of different types.
varchar 64   normal meta.code.class
The frame types and their abbreviations are:
 confidence = "conf" dark = "dark" deep = "deep" difference = "diff" filtered = "filt" flat = "flat" interleaved = "leav" mosaic = "mosaic" sky = "sky" stack = "stack" default value = "normal"
frinID [nspid]Multiframe WSA NonSurvey UID of library calibration fringe frame bigint 8   -99999999 obs.field
fsTemp [nspid]Multiframe WSA NonSurvey CCC 1st stage temperature {image primary HDU keyword: FS_TEMP} real 4 degrees_Kelvin -0.9999995e9

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