Get high-fidelity stars in given area
Goal/Description
We want to get all objects with 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.
Query
SELECT o.ObjID,x.ra,x.dec, o.gMeanPSFMag, o.gMeanPSFMagErr, o.rMeanPSFMag, o.rMeanPSFMagErr, o.iMeanPSFMag, o.iMeanPSFMagErr, 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) 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
Results
1218 objects, here are a few:
[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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
107763339788289399 | 333.97880748 | -0.19253748 | 21.9438 | 0.11106 | 20.2034 | 0.027063 | 19.5014 | 0.017971 | 19.0134 | 0.029953 | 8 | 14 | 13 | 8 | 24888 | 0.998615 | 24888 | 0.999312 | 24888 | 0.999381 | 24888 | 0.999301 |
107763340006649595 | 334.00065785 | -0.19236407 | 19.5995 | 0.017734 | 18.3372 | 0.007714 | 17.2107 | 0.004049 | 16.692 | 0.005484 | 12 | 12 | 12 | 10 | 16696 | 0.999146 | 16696 | 0.999319 | 312 | 0.998627 | 312 | 0.999408 |
107763340150042386 | 334.01499455 | -0.19836754 | 22.0476 | 0.160347 | 20.8012 | 0.045506 | 19.6133 | 0.008253 | 19.0216 | 0.030203 | 10 | 10 | 9 | 8 | 16696 | 0.99887 | 16696 | 0.999343 | 16688 | 0.999042 | 16696 | 0.999635 |
107763340365706349 | 334.03655623 | -0.19508464 | 21.9288 | 0.109147 | 21.5784 | 0.086763 | 21.0964 | 0.066446 | 20.3965 | 0.118844 | 12 | 10 | 13 | 13 | 16696 | 0.999101 | 16696 | 0.998961 | 24888 | 0.999252 | 16696 | 0.999638 |
107763340427936906 | 334.04279614 | -0.1945979 | 21.7427 | 0.092382 | 21.6415 | 0.091627 | 20.9072 | 0.058188 | 21.2196 | 0.173467 | 12 | 10 | 13 | 1 | 16696 | 0.998822 | 24888 | 0.999251 | 24888 | 0.999312 | 16688 | 0.996947 |
107773339339728631 | 333.93396938 | -0.1848365 | 20.9823 | 0.048723 | 19.8663 | 0.02085 | 18.5813 | 0.003806 | 17.9914 | 0.01321 | 12 | 14 | 12 | 12 | 16696 | 0.999034 | 16696 | 0.998801 | 16688 | 0.999195 | 16696 | 0.999352 |
107773339418267057 | 333.94184362 | -0.18615982 | 21.9966 | 0.070236 | 20.9456 | 0.026134 | 20.5547 | 0.019016 | 20.2507 | 0.090782 | 4 | 6 | 8 | 6 | 16696 | 0.998274 | 16696 | 0.99932 | 16688 | 0.999224 | 16696 | 0.99931 |
107773339602449349 | 333.96024341 | -0.18424616 | 20.9886 | 0.025514 | 19.7236 | 0.010273 | 18.4433 | 0.008773 | 17.8297 | 0.004637 | 6 | 6 | 10 | 10 | 16688 | 0.998774 | 16688 | 0.998944 | 16696 | 0.999612 | 16688 | 0.999226 |
107773339666322634 | 333.96662932 | -0.18984629 | 18.294 | 0.00797 | 17.7351 | 0.005475 | 17.5201 | 0.004917 | 17.3994 | 0.005004 | 8 | 10 | 10 | 6 | 16696 | 0.998668 | 16696 | 0.998606 | 16696 | 0.999246 | 16688 | 0.998957 |
107773339901141449 | 333.99012708 | -0.19081259 | 22.0177 | 0.116995 | 20.7935 | 0.043956 | 19.3285 | 0.015997 | 18.5802 | 0.021108 | 12 | 13 | 13 | 8 | 16696 | 0.999287 | 16696 | 0.999454 | 16696 | 0.998564 | 16696 | 0.999272 |
107773339922295075 | 333.99222737 | -0.1878054 | 17.7974 | 0.005823 | 17.017 | 0.003604 | 16.7225 | 0.003064 | 16.5933 | 0.005041 | 12 | 14 | 13 | 12 | 16696 | 0.998374 | 312 | 0.999163 | 16696 | 0.999453 | 16696 | 0.998986 |
107773340001252574 | 334.00013929 | -0.1898869 | 21.3841 | 0.069917 | 20.0722 | 0.024994 | 19.5129 | 0.018361 | 19.1246 | 0.031388 | 12 | 12 | 12 | 13 | 24888 | 0.998421 | 24888 | 0.999319 | 24888 | 0.999253 | 24888 | 0.999294 |
107773340107578040 | 334.01075805 | -0.18533095 | 17.109 | 0.004059 | 16.5449 | 0.002871 | 16.302 | 0.002455 | 16.1988 | 0.003986 | 10 | 10 | 13 | 12 | 312 | 0.999051 | 16696 | 0.999254 | 16696 | 0.999073 | 16696 | 0.999466 |
107773340109724544 | 334.01096984 | -0.1882432 | 21.7254 | 0.093256 | 20.3291 | 0.031514 | 19.4678 | 0.017949 | 19.0293 | 0.028591 | 10 | 8 | 11 | 14 | 16696 | 0.998972 | 16696 | 0.99931 | 312 | 0.99966 | 16696 | 0.999251 |
107773340114740336 | 334.0114756 | -0.1917495 | 17.2637 | 0.004318 | 16.6291 | 0.002965 | 16.3879 | 0.002633 | 16.2866 | 0.005652 | 14 | 12 | 8 | 9 | 16696 | 0.999122 | 16696 | 0.998728 | 312 | 0.998627 | 16696 | 0.99946 |
107773340169799582 | 334.01700027 | -0.18402969 | 22.1014 | 0.180304 | 21.6907 | 0.096317 | 21.5285 | 0.100084 | 21.1292 | 0.074905 | 1 | 12 | 11 | 5 | 16688 | 0.997868 | 16696 | 0.998527 | 16696 | 0.999522 | 16688 | 0.999 |
107773340329630606 | 334.03296122 | -0.19152157 | 18.8353 | 0.010784 | 17.7533 | 0.005436 | 17.2459 | 0.004162 | 16.9953 | 0.006417 | 11 | 13 | 12 | 12 | 16696 | 0.999285 | 16696 | 0.998643 | 16696 | 0.998812 | 16696 | 0.999008 |
107773340376450699 | 334.03766653 | -0.19141893 | 22.0022 | 0.175436 | 21.661 | 0.092915 | 21.4471 | 0.03274 | 20.7124 | 0.164865 | 1 | 12 | 10 | 13 | 24888 | 0.99772 | 24888 | 0.998626 | 24880 | 0.999248 | 16696 | 0.999193 |
107773340386725842 | 334.03867183 | -0.18715179 | 19.4999 | 0.016539 | 18.2269 | 0.007123 | 17.1339 | 0.00386 | 16.6389 | 0.005113 | 11 | 14 | 13 | 12 | 16696 | 0.999222 | 16696 | 0.999259 | 16696 | 0.999417 | 16696 | 0.999315 |
107783339193787892 | 333.91937678 | -0.17711893 | 17.9753 | 0.00649 | 17.0417 | 0.003684 | 16.591 | 0.002929 | 16.3326 | 0.001935 | 8 | 10 | 12 | 10 | 16696 | 0.998422 | 16696 | 0.99943 | 16696 | 0.998856 | 16688 | 0.999388 |
107783339506531799 | 333.95065363 | -0.18220902 | 21.6765 | 0.090922 | 20.4441 | 0.03417 | 19.0721 | 0.01366 | 18.4361 | 0.006853 | 4 | 6 | 10 | 10 | 16696 | 0.998606 | 16696 | 0.999008 | 16696 | 0.998315 | 16688 | 0.999236 |
107783339643590084 | 333.96436482 | -0.18362926 | 21.1812 | 0.059382 | 20.2582 | 0.029123 | 19.8413 | 0.024021 | 19.6109 | 0.017679 | 8 | 10 | 11 | 7 | 16696 | 0.998747 | 16696 | 0.999258 | 16696 | 0.999268 | 16688 | 0.998948 |
107783339772216012 | 333.9772202 | -0.17869469 | 17.4821 | 0.004907 | 16.3959 | 0.002577 | 15.8533 | 0.001961 | 15.5827 | 0.003713 | 10 | 14 | 12 | 11 | 16696 | 0.998487 | 16696 | 0.999441 | 16696 | 0.999079 | 16696 | 0.999014 |
Galaxy Candidates for K2 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.
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 INTO mydb.[K2C14pv3_v3] FROM MeanObject AS o JOIN fgetNearbyObjEq(160.68333, 6.85167 , 8.5) cone ON cone.objid = o.objID JOIN ObjectThin AS ot ON ot.objID = o.objID JOIN StackPetrosian AS sp ON sp.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 K2C14pv3_v3
objID | raStack | decStack | raMean | decMean | ng | gMeanPSFMag | gMeanPSFMagErr | gMeanKronMag | gMeanKronMagErr | nr | rMeanPSFMag | rMeanPSFMagErr | rMeanKronMag | rMeanKronMagErr | ni | iMeanPSFMag | iMeanPSFMagErr | iMeanKronMag | iMeanKronMagErr | nz | zMeanPSFMag | zMeanPSFMagErr | zMeanKronMag | zMeanKronMagErr | ny | yMeanPSFMag | yMeanPSFMagErr | yMeanKronMag | yMeanKronMagErr | gQfPerfect | rQfPerfect | iQfPerfect | zQfPerfect | yQfPerfect | qualityFlag | objInfoFlag | gpetRadius | rpetRadius | ipetRadius | zpetRadius | ypetRadius | gpetR50 | rpetR50 | ipetR50 | zpetR50 | ypetR50 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
123311549281210477 | 154.92812611 | 12.75831367 | 154.92811431 | 12.75832518 | 10 | 20.0835 | 0.024401 | 19.301 | 0.028161 | 16 | 19.0415 | 0.039718 | 18.209 | 0.009343 | 20 | 18.6326 | 0.023309 | 17.7053 | 0.011849 | 12 | 18.0135 | 0.029643 | 17.4283 | 0.009914 | 14 | 18.1077 | 0.042534 | 17.3281 | 0.023553 | 0.999812 | 0.99953 | 0.999408 | 0.999818 | 0.999398 | 61 | 512024576 | 7.65464 | 6.89781 | 5.74412 | 5.57977 | 4.21074 | 2.78133 | 2.36178 | 2.05482 | 2.0514 | 1.67238 |
123311556359837190 | 155.63595456 | 12.76391817 | 155.63594254 | 12.76393065 | 17 | 20.6317 | 0.029612 | 20.1322 | 0.030872 | 22 | 19.5872 | 0.035236 | 19.009 | 0.015546 | 24 | 19.2038 | 0.022632 | 18.5463 | 0.013514 | 13 | 18.7241 | 0.029605 | 18.2488 | 0.016407 | 15 | 18.7148 | 0.025143 | 18.1658 | 0.036589 | 0.999841 | 0.999667 | 0.999723 | 0.999696 | 0.999486 | 61 | 512024576 | 4.11764 | 3.19234 | 3.37176 | 3.55703 | 3.55401 | 1.64643 | 1.28659 | 1.30457 | 1.41352 | 1.64367 |
123311563608493897 | 156.36080172 | 12.76123503 | 156.36081918 | 12.76123267 | 12 | 19.6828 | 0.053324 | 17.5969 | 0.013996 | 10 | 19.6995 | 0.214609 | 17.2401 | 0.014849 | 14 | 19.6599 | 0.088526 | 17.1426 | 0.013593 | 6 | 19.2574 | 0.077356 | 17.3268 | 0.023463 | 5 | 19.4923 | 0.214712 | 17.9344 | 0.035796 | 0.999578 | 0.999805 | 0.999771 | 0.999548 | 0.998263 | 53 | 444915712 | 10.8759 | 11.199 | 11.1484 | 10.5347 | 9.50867 | 4.43794 | 4.40708 | 4.52688 | 4.21262 | 3.28768 |
123311564766811688 | 156.47673248 | 12.75933957 | 156.47676479 | 12.75933774 | 10 | 20.6036 | 0.038835 | 20.2254 | 0.028823 | 12 | 19.6161 | 0.029449 | 19.0641 | 0.026325 | 19 | 19.2917 | 0.021417 | 18.7812 | 0.016662 | 9 | 18.9118 | 0.021025 | 18.5409 | 0.035919 | 12 | 18.8295 | 0.024847 | 18.3664 | 0.05601 | 0.99962 | 0.999673 | 0.999685 | 0.999586 | 0.99939 | 53 | 444915712 | 4.00564 | 3.49143 | 4.64069 | 4.7902 | -9.08956 | 1.3887 | 1.11921 | 1.25359 | 1.44259 | -9.07378 |
123311565993385602 | 156.59931289 | 12.76262096 | 156.59931872 | 12.76262302 | 10 | 19.3503 | 0.026542 | 18.4721 | 0.022539 | 14 | 18.8037 | 0.032864 | 18.0029 | 0.020426 | 15 | 18.5567 | 0.012024 | 17.6336 | 0.012315 | 11 | 18.3297 | 0.061319 | 17.387 | 0.016721 | 17 | 18.2486 | 0.053074 | 17.2516 | 0.087225 | 0.999834 | 0.999308 | 0.999417 | 0.999812 | 0.999327 | 61 | 512024576 | 3.11429 | 2.81284 | 2.97854 | 3.117 | 8.68295 | 1.00833 | 0.980632 | 1.07104 | 0.984775 | 2.965 |
123311564444738006 | 156.44447917 | 12.76459414 | 156.44450277 | 12.76459045 | 9 | 19.9533 | 0.026005 | 19.4565 | 0.018023 | 14 | 18.9669 | 0.017971 | 18.2664 | 0.011821 | 14 | 18.555 | 0.01805 | 17.7932 | 0.00674 | 7 | 18.1525 | 0.048449 | 17.5099 | 0.01958 | 16 | 18.0324 | 0.054899 | 17.3798 | 0.021401 | 0.999658 | 0.999581 | 0.999657 | 0.999516 | 0.999339 | 61 | 512024576 | 4.85821 | 4.28138 | 4.09561 | 3.92565 | 4.57184 | 1.85211 | 1.5708 | 1.53056 | 1.5795 | 1.66867 |
123311566009794928 | 156.60102574 | 12.76200927 | 156.60102304 | 12.76201848 | 10 | 19.036 | 0.059096 | 18.0817 | 0.02248 | 14 | 17.9479 | 0.044041 | 17.1254 | 0.015734 | 14 | 17.6411 | 0.023534 | 16.7142 | 0.006589 | 9 | 17.2256 | 0.057247 | 16.4349 | 0.005736 | 16 | 17.0858 | 0.055096 | 16.1444 | 0.03247 | 0.999666 | 0.999091 | 0.999663 | 0.999464 | 0.998955 | 61 | 512024576 | 5.99277 | 5.45115 | 4.69198 | 5.3783 | 2.76549 | 2.43662 | 2.2388 | 1.8653 | 2.15231 | 1.06782 |
123311566048421351 | 156.60484481 | 12.75900132 | 156.60484546 | 12.7590098 | 8 | 19.2369 | 0.034491 | 18.727 | 0.024239 | 12 | 18.3925 | 0.01095 | 17.7554 | 0.010232 | 16 | 18.0391 | 0.028661 | 17.3429 | 0.005609 | 11 | 17.6664 | 0.024755 | 17.0823 | 0.010994 | 18 | 17.5588 | 0.034835 | 17.0271 | 0.011815 | 0.999664 | 0.999544 | 0.999602 | 0.999623 | 0.999433 | 61 | 512024576 | 4.16681 | 3.54043 | 3.21858 | 3.69755 | 26.1036 | 1.56839 | 1.36376 | 1.27506 | 1.37458 | -999 |
123311569207978960 | 156.92076482 | 12.76540801 | 156.92076056 | 12.76544365 | 9 | 20.8824 | 0.063935 | 20.1254 | 0.038368 | 12 | 20.1992 | 0.028047 | 19.3953 | 0.02682 | 14 | 19.963 | 0.017104 | 19.1169 | 0.022385 | 11 | 19.3916 | 0.093169 | 18.8423 | 0.047232 | 14 | 19.3703 | 0.050302 | 18.6263 | 0.060436 | 0.999512 | 0.999706 | 0.999858 | 0.999353 | 0.999578 | 53 | 444915712 | 5.58858 | 4.86715 | 4.59496 | 5.16338 | 4.85984 | 2.10445 | 2.04828 | 1.82724 | 2.16979 | 2.12018 |
123311577928974093 | 157.79288195 | 12.76131325 | 157.79286038 | 12.76133682 | 10 | 19.8432 | 0.026994 | 19.4151 | 0.037358 | 14 | 19.0266 | 0.010783 | 18.5078 | 0.00961 | 20 | 18.6403 | 0.014359 | 18.1007 | 0.008081 | 15 | 18.302 | 0.028949 | 17.9015 | 0.016442 | 14 | 18.3422 | 0.025802 | 17.8278 | 0.00872 | 0.999293 | 0.999235 | 0.999065 | 0.999243 | 0.999374 | 61 | 512024576 | 13.2731 | 3.46466 | 3.09503 | 3.8395 | 2.8104 | 6.11566 | 1.3939 | 1.23334 | 1.3563 | 1.08618 |
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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