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Get high-fidelity stars in given area

Goal/Description

We want to get all objects within R degrees of a given position that are high fidelity stellar-like objects.

We get all objects within 0.2 degree of RA=334.0 and Dec=0.0 which have mean magnitudes in griz (i.e. at least 1 detection in each band that can be used for the mean mag). In addition, we require QfPerfect>0.85 in all bands.

Note that the search radius is in arcminutes.  This is a recent change in the interface to make the PS1 database functions consistent with those used for SDSS, GALEX, Kepler, the HSC, and other MAST catalogs.

Query

SELECT o.ObjID,x.ra,x.dec, 
o.gMeanPSFMag, o.gMeanPSFMagErr,
o.rMeanPSFMag, o.rMeanPSFMagErr, 
o.iMeanPSFMag, o.iMeanPSFMagErr, o.iMeanKronMag,
o.zMeanPSFMag, o.zMeanPSFMagErr,
o.gMeanPSFmagNpt,o.rMeanPSFmagNpt,o.iMeanPSFmagNpt,o.zMeanPSFmagNpt,
o.gFlags, o.gQfPerfect,
o.rFlags, o.rQfPerfect,
o.iFlags, o.iQfPerfect,
o.zFlags, o.zQfPerfect
FROM dbo.fGetNearbyObjEq(334, 0.0, 0.2*60) as x
JOIN MeanObject o on o.ObjID=x.ObjId
where o.gMeanPSFmagNpt>=1 and o.rMeanPSFmagNpt>=1 and o.iMeanPSFmagNpt>=1 and o.zMeanPSFmagNpt>=1
and o.gQfPerfect>=0.85 and o.rQfPerfect>=0.85 and o.iQfPerfect>=0.85 and o.zQfPerfect>=0.85
and o.iMeanPSFMag - o.iMeanKronMag < 0.05

Results

1218 objects, here are the first 10:

[ObjID]:Int64[ra]:Double[dec]:Double[gMeanPSFMag]:Single[gMeanPSFMagErr]:Single[rMeanPSFMag]:Single[rMeanPSFMagErr]:Single[iMeanPSFMag]:Single[iMeanPSFMagErr]:Single[zMeanPSFMag]:Single[zMeanPSFMagErr]:Single[gMeanPSFmagNpt]:Int16[rMeanPSFmagNpt]:Int16[iMeanPSFmagNpt]:Int16[zMeanPSFmagNpt]:Int16[gFlags]:Int32[gQfPerfect]:Single[rFlags]:Int32[rQfPerfect]:Single[iFlags]:Int32[iQfPerfect]:Single[zFlags]:Int32[zQfPerfect]:Single
107763339788289399333.97880748-0.1925374821.94380.1110620.20340.02706319.50140.01797119.01340.029953814138248880.998615248880.999312248880.999381248880.999301
107763340006649595334.00065785-0.1923640719.59950.01773418.33720.00771417.21070.00404916.6920.00548412121210166960.999146166960.9993193120.9986273120.999408
107763340150042386334.01499455-0.1983675422.04760.16034720.80120.04550619.61330.00825319.02160.030203101098166960.99887166960.999343166880.999042166960.999635
107763340365706349334.03655623-0.1950846421.92880.10914721.57840.08676321.09640.06644620.39650.11884412101313166960.999101166960.998961248880.999252166960.999638
107763340427936906334.04279614-0.194597921.74270.09238221.64150.09162720.90720.05818821.21960.1734671210131166960.998822248880.999251248880.999312166880.996947
107773339339728631333.93396938-0.184836520.98230.04872319.86630.0208518.58130.00380617.99140.0132112141212166960.999034166960.998801166880.999195166960.999352
107773339418267057333.94184362-0.1861598221.99660.07023620.94560.02613420.55470.01901620.25070.0907824686166960.998274166960.99932166880.999224166960.99931
107773339602449349333.96024341-0.1842461620.98860.02551419.72360.01027318.44330.00877317.82970.004637661010166880.998774166880.998944166960.999612166880.999226
107773339666322634333.96662932-0.1898462918.2940.0079717.73510.00547517.52010.00491717.39940.005004810106166960.998668166960.998606166960.999246166880.998957
107773339901141449333.99012708-0.1908125922.01770.11699520.79350.04395619.32850.01599718.58020.0211081213138166960.999287166960.999454166960.998564166960.999272

 

Galaxy Candidates for K2 C14 SN Search

Goal/Description

The Kepler Extra-Galactic Survey (KEGS) is a program using the Kepler telescope to search for supernovae, active galactic nuclei, and other transients in galaxies. We have to identify galaxies in a suitable redshift range (z<=0.12) a priori, which will be monitored by K2. Here is an example to get galaxies for Campaign 14. We only select objects with r<=19.5, and we make a cut on (rmeanpsfmag - rmeankronmag)>=0.5 in order to remove stars. We only want to use objects for which the majority of pixels were not masked, thus the cut on QFperfect>=0.95. We also obtain the petrosian radii in order to be able to select galaxies by size. This query gives us an initial list of galaxies which then gets vetted more, e.g. with cuts on qualityFlag and objInfoFlag and information from external catalogs.

Query

SELECT  o.objID,
ot.raStack, ot.decStack, ot.raMean, ot.decMean, 
ot.ng,  o.gMeanPSFMag,o.gMeanPSFMagErr,o.gMeanKronMag,o.gMeanKronMagErr,
ot.nr,  o.rMeanPSFMag,o.rMeanPSFMagErr,o.rMeanKronMag,o.rMeanKronMagErr,
ot.ni,  o.iMeanPSFMag,o.iMeanPSFMagErr,o.iMeanKronMag,o.iMeanKronMagErr,
ot.nz,  o.zMeanPSFMag,o.zMeanPSFMagErr,o.zMeanKronMag,o.zMeanKronMagErr,
ot.ny,  o.yMeanPSFMag,o.yMeanPSFMagErr,o.yMeanKronMag,o.yMeanKronMagErr,
o.gQfPerfect,o.rQfPerfect,o.iQfPerfect,o.zQfPerfect,o.yQfPerfect,
ot.qualityFlag,ot.objInfoFlag,
sp.gpetRadius,sp.rpetRadius,sp.ipetRadius,sp.zpetRadius,sp.ypetRadius,
sp.gpetR50,sp.rpetR50,sp.ipetR50,sp.zpetR50,sp.ypetR50,
soa.gpsfLikelihood, soa.gKronRad, soa.rKronRad, soa.iKronRad
       INTO mydb.[K2C14] FROM MeanObject AS o
       JOIN fgetNearbyObjEq(160.68333, 6.85167 , 8.5*60) cone ON cone.objid = o.objID 
       JOIN ObjectThin AS ot ON ot.objID = o.objID
       LEFT JOIN StackPetrosian AS sp ON sp.objID = o.objID
       LEFT JOIN StackObjectAttributes AS soa ON soa.objID = o.objID
     WHERE ot.ni >= 3
     AND ot.ng >= 3
     AND ot.nr >= 3
     AND (o.rMeanKronMag > 0 AND o.rMeanKronMag <= 19.5 )
     AND (o.gQfPerfect >= 0.95)
     AND (o.rQfPerfect >= 0.95)
     AND (o.iQfPerfect >= 0.95)
     AND (o.zQfPerfect >= 0.95)
     AND (o.rmeanpsfmag - o.rmeankronmag > 0.5)

Results

First 10 Rows on MyDB Table K2C14

 
objIDraStackdecStackraMeandecMeannggMeanPSFMaggMeanPSFMagErrgMeanKronMaggMeanKronMagErrnrrMeanPSFMagrMeanPSFMagErrrMeanKronMagrMeanKronMagErrniiMeanPSFMagiMeanPSFMagErriMeanKronMagiMeanKronMagErrnzzMeanPSFMagzMeanPSFMagErrzMeanKronMagzMeanKronMagErrnyyMeanPSFMagyMeanPSFMagErryMeanKronMagyMeanKronMagErrgQfPerfectrQfPerfectiQfPerfectzQfPerfectyQfPerfectqualityFlagobjInfoFlaggpetRadiusrpetRadiusipetRadiuszpetRadiusypetRadiusgpetR50rpetR50ipetR50zpetR50ypetR50gpsfLikelihoodgKronRadrKronRadiKronRad
109081656511207587165.651237990.90602251165.65123160.90600456721.64370.36786520.04930.0585671220.65740.18440619.39620.0720332120.59460.07808219.19980.0261731120.56990.07655219.11580.033139220.240.00860819.10510.2027960.9997340.9994270.9995210.9993960.9974685344491571210.42674.6100414.12256.215495.260254.089351.714275.207232.503062.9793605.316114.991346.30024
109081657271754110165.727168550.90305714165.72715640.903043721420.13350.05522519.37790.0270961819.10850.05028217.94620.022754118.36190.03444217.40180.0063971418.40430.02648317.16190.010811518.08930.0277317.13870.0210280.9998180.9996660.9997560.9992830.9992136151202457617.39587.082746.859666.214116.624785.230862.768162.693692.502312.4374206.147827.207998.64767
109081660112548426166.011254190.90667221166.011229910.906672741019.67370.01875619.13820.0557951419.01170.02988518.45360.0463323718.43670.02598917.94020.0114791218.4730.00992717.78090.0200741518.19920.01773617.58470.0255770.9997270.9995910.9997510.9989760.99934615120245764.127764.207983.300793.079223.512891.669211.625551.300191.182621.261711.10623E-284.502344.898164.61272
109081660112548426166.011254190.90667221166.011229910.906672741019.67370.01875619.13820.0557951419.01170.02988518.45360.0463323718.43670.02598917.94020.0114791218.4730.00992717.78090.0200741518.19920.01773617.58470.0255770.9997270.9995910.9997510.9989760.99934615120245764.127764.207983.300793.079223.512891.669211.625551.300191.182621.261718.32451E-384.506764.955314.66793
109081660112548426166.011254190.90667221166.011229910.906672741019.67370.01875619.13820.0557951419.01170.02988518.45360.0463323718.43670.02598917.94020.0114791218.4730.00992717.78090.0200741518.19920.01773617.58470.0255770.9997270.9995910.9997510.9989760.99934615120245764.451584.515673.299453.263783.356851.716031.699791.290041.214741.320691.10623E-284.502344.898164.61272
109081660112548426166.011254190.90667221166.011229910.906672741019.67370.01875619.13820.0557951419.01170.02988518.45360.0463323718.43670.02598917.94020.0114791218.4730.00992717.78090.0200741518.19920.01773617.58470.0255770.9997270.9995910.9997510.9989760.99934615120245764.451584.515673.299453.263783.356851.716031.699791.290041.214741.320698.32451E-384.506764.955314.66793
109081663334564862166.333503430.90367258166.333523840.903649831120.10580.07697518.74240.0180331819.84830.04617118.23470.0287032619.40880.04793317.97610.0125731219.8440.05079218.18780.050531019.49140.07334418.1280.0603830.9996170.99960.9996370.9994390.998929534449157126.443186.3457847.4016.38315.810852.483342.50278-9992.167521.9412906.672846.624357.05478
109091546911366908154.691110520.91371481154.691116130.913722151221.52110.06192520.63930.0387231219.87120.03321419.13340.037061819.36710.02713818.63870.0201941219.1640.03295418.34980.0215661818.77810.02905118.13540.0617770.9996710.9995620.9993270.9990420.99935615120245765.934786.476884.993034.200244.273732.770742.312051.874841.626621.4579404.032145.360955.44253
109091553299077983155.329859960.91460413155.329888860.914582041321.06220.03455420.3840.0257491219.74960.02952919.14910.0183161819.37540.01425618.6970.0189411119.09070.01483618.41810.0228081518.78320.0351618.27930.0441250.9996670.9996340.9992290.9991760.999643534449157123.557874.135223.153722.800533.066621.629891.616361.254561.093981.1970603.31494.098513.95382
109091554897205761-999-999155.489600690.91276146521.34090.09619219.67640.076313420.73410.21086119.24420.062298420.80520.25307219.56610.124974321.22650.12146819.88650.0942780-999-999-999-9990.9991390.999230.9988890.997634-99937310697984              

 

 


Sample Query 1 template (this is a template, just cut and paste, do not modify)

Goal/Description

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Query

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Results

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