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We want to get all objects within 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, and PSF-Kron to be < 0.05 to select only stars (we use the i-band in this example, as this is usually the deepest). We select stars with the difference between Kron and PSF magnitude as described FAQ.
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
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Results
696 objects, here are the first 10:
107763340006509592 | 334.00067427 | -0.19239401 | 0.00192 | 0.00245 | 60 | 19.6313 | 0.007621 | 14 | 18.3653 | 0.006528 | 15 | 17.2444 | 0.002347 | 26 | 16.7225 | 0.002455 | 12 | 16.4851 | 0.015898 | 13 | 18.4699 | 0.009761 | 93 | 16 | 17 | 34 | 12 | 14 | 115000 | 0.999873 | 115000 | 0.999291 | 115000 | 0.999634 | 115000 | 0.999044 | 115000 | 0.999622 | 1 | 1 |
107763340149672418 | 334.01502523 | -0.19839402 | 0.00518 | 0.00285 | 52 | 21.9751 | 0.083929 | 3 | 20.7948 | 0.031693 | 15 | 19.6486 | 0.013022 | 17 | 19.1114 | 0.012857 | 12 | 18.8021 | 0.026564 | 14 | 20.9895 | 0.053647 | 65 | 3 | 15 | 20 | 13 | 14 | 115000 | 0.999462 | 115000 | 0.999644 | 115000 | 0.999309 | 115000 | 0.999628 | 115000 | 0.998982 | 1 | 1 |
107763340365446394 | 334.03656773 | -0.1951062 | 0.02425 | 0.02707 | 52 | 21.8092 | 0.062935 | 6 | 21.3268 | 0.0751 | 12 | 21.0911 | 0.027663 | 24 | 20.5195 | 0.04418 | 10 | 19.3565 | 0.000343 | 1 | 21.4159 | 0.068216 | 63 | 8 | 14 | 29 | 10 | 2 | 16892216 | 0.999325 | 115000 | 0.999636 | 115000 | 0.999367 | 115000 | 0.999632 | 16892216 | 0.998982 | 1 | 1 |
107773339339718616 | 333.93399766 | -0.18485765 | 0.00422 | 0.00798 | 60 | 21.0967 | 0.057864 | 12 | 19.896 | 0.011242 | 12 | 18.614 | 0.00688 | 29 | 18.0234 | 0.004613 | 10 | 17.7739 | 0.022577 | 9 | 20.0117 | 0.031177 | 85 | 12 | 12 | 37 | 12 | 12 | 115000 | 0.999404 | 115000 | 0.999715 | 115000 | 0.999856 | 115000 | 0.999515 | 115000 | 0.999526 | 1 | 1 |
107773339418197028 | 333.94187857 | -0.18614146 | 0.01971 | 0.03959 | 52 | 21.5705 | 0.012027 | 2 | 20.981 | 0.008091 | 6 | 20.586 | 0.02858 | 27 | 20.3225 | 0.049434 | 8 | 20.3266 | 0.143002 | 1 | 20.9779 | 0.153102 | 60 | 4 | 9 | 35 | 11 | 1 | 115000 | 0.999 | 115000 | 0.999029 | 115000 | 0.999628 | 115000 | 0.999253 | 114992 | 0.997765 | 1 | 1 |
107773339602519327 | 333.96026447 | -0.18427345 | 0.00452 | 0.0079 | 60 | 21.079 | 0.018093 | 6 | 19.7609 | 0.014422 | 6 | 18.456 | 0.004637 | 21 | 17.8612 | 0.006005 | 10 | 17.6079 | 0.035327 | 6 | 19.8238 | 0.015897 | 65 | 8 | 11 | 25 | 10 | 11 | 115000 | 0.999168 | 115000 | 0.999158 | 115000 | 0.999613 | 115000 | 0.999212 | 115000 | 0.998133 | 1 | 1 |
107773339666312601 | 333.96664968 | -0.1898753 | 0.00262 | 0.00237 | 60 | 18.3317 | 0.005523 | 14 | 17.7711 | 0.002763 | 14 | 17.531 | 0.003137 | 27 | 17.4243 | 0.006355 | 7 | 17.3689 | 0.016866 | 8 | 17.8509 | 0.00506 | 77 | 14 | 16 | 29 | 8 | 10 | 115000 | 0.999479 | 115000 | 0.999742 | 115000 | 0.999548 | 115000 | 0.99868 | 115000 | 0.998906 | 1 | 1 |
107773339901521365 | 333.99016194 | -0.19086722 | 0.00392 | 0.00732 | 52 | 21.9495 | 0.067887 | 9 | 20.8339 | 0.024821 | 13 | 19.3754 | 0.005145 | 33 | 18.642 | 0.01119 | 12 | 18.2968 | 0.0151 | 12 | 20.8794 | 0.082054 | 81 | 9 | 14 | 33 | 13 | 12 | 115000 | 0.999095 | 115000 | 0.999729 | 115000 | 0.999428 | 115000 | 0.999706 | 115000 | 0.99911 | 1 | 1 |
107773339922175058 | 333.99225189 | -0.1878471 | 0.00356 | 0.02173 | 60 | 17.8118 | 0.005868 | 12 | 17.0528 | 0.002585 | 12 | 16.7543 | 0.0022 | 26 | 16.6207 | 0.00568 | 12 | 16.5089 | 0.009786 | 12 | 17.1191 | 0.003335 | 87 | 14 | 14 | 31 | 14 | 14 | 115000 | 0.999704 | 115000 | 0.999442 | 115000 | 0.999577 | 115000 | 0.999238 | 115000 | 0.999307 | 1 | 1 |
107773340107508029 | 334.01078438 | -0.18536236 | 0.0057 | 0.0057 | 60 | 17.1406 | 0.001898 | 12 | 16.5837 | 0.002945 | 13 | 16.3375 | 0.001891 | 22 | 16.2293 | 0.002224 | 13 | 16.146 | 0.008295 | 9 | 16.6436 | 0.001166 | 87 | 16 | 15 | 30 | 14 | 12 | 115000 | 0.999684 | 115000 | 0.999464 | 115000 | 0.999912 | 115000 | 0.999435 | 115000 | 0.999022 | 1 | 1 |
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
Results
First 10 out of 161,920 Rows of MyDB Table C14
125391633203089697 | 163.32030341 | 14.49934933 | 163.32028882 | 14.49933092 | 10 | 21.0093 | 0.036089 | 20.6731 | 0.074827 | 14 | 19.7703 | 0.026601 | 19.2563 | 0.01528 | 10 | 19.2681 | 0.011538 | 18.6397 | 0.021455 | 14 | 18.9957 | 0.016901 | 18.3729 | 0.01564 | 13 | 18.6968 | 0.030642 | 18.1266 | 0.037591 | 0.999566 | 0.999635 | 0.999213 | 0.999209 | 0.999349 | 61 | 512024576 | 8.6166 | 2.71981 | 2.66062 | 2.67084 | 2.77483 | 1.25887 | 1.19192 | 1.08737 | 1.0683 | 1.02985 | 1 | 1 |
125391634958624964 | 163.49586429 | 14.49541362 | 163.49583602 | 14.4954125 | 10 | 20.3336 | 0.030038 | 19.5583 | 0.020455 | 14 | 19.7966 | 0.03558 | 18.9214 | 0.022374 | 15 | 19.5316 | 0.025872 | 18.6278 | 0.009325 | 13 | 19.367 | 0.033795 | 18.5621 | 0.032986 | 14 | 19.1271 | 0.038272 | 18.3829 | 0.051101 | 0.999619 | 0.999457 | 0.998965 | 0.999023 | 0.999377 | 53 | 444915712 | 5.27407 | 5.04787 | 4.27913 | 4.71588 | 5.21124 | 2.08763 | 1.80538 | 1.5933 | 1.46589 | 3.27605 | 1 | 1 |
125391635282559255 | 163.52826527 | 14.49899141 | 163.52823941 | 14.49899527 | 8 | 20.9598 | 0.062091 | 20.1741 | 0.026735 | 14 | 20.3923 | 0.062902 | 19.417 | 0.029026 | 15 | 20.0727 | 0.036366 | 19.0168 | 0.024443 | 11 | 19.8652 | 0.025211 | 18.8208 | 0.031221 | 8 | 19.2406 | 0.092635 | 18.7778 | 0.141102 | 0.999726 | 0.999435 | 0.9992 | 0.999113 | 0.999185 | 53 | 444915712 | 4.60435 | 5.28031 | 4.94089 | 5.31433 | 4.2082 | 2.20738 | 2.36534 | 2.07909 | 2.09779 | 1.98962 | 1 | 1 |
125391636122615271 | 163.61224801 | 14.49564107 | 163.61222312 | 14.4956375 | 10 | 21.0158 | 0.045075 | 20.3179 | 0.073374 | 16 | 20.0838 | 0.039024 | 19.361 | 0.032028 | 23 | 19.6738 | 0.024572 | 18.9632 | 0.014486 | 13 | 19.4487 | 0.020207 | 18.7097 | 0.024255 | 12 | 19.2102 | 0.063186 | 18.5949 | 0.073146 | 0.999603 | 0.999763 | 0.999656 | 0.998814 | 0.999592 | 53 | 444915712 | 3.83093 | 3.52666 | 3.35348 | 4.64719 | 3.17508 | 1.43311 | 1.45691 | 1.24687 | 1.44055 | 1.13158 | 1 | 1 |
118791643648704345 | 164.36486427 | 8.99489954 | 164.36488729 | 8.99490711 | 12 | 19.1429 | 0.052652 | 18.2254 | 0.01775 | 10 | 18.1585 | 0.047254 | 17.1922 | 0.010684 | 24 | 17.8359 | 0.014582 | 16.7399 | 0.006493 | 15 | 17.529 | 0.034207 | 16.475 | 0.016307 | 12 | 17.3821 | 0.032685 | 16.5217 | 0.016764 | 0.999406 | 0.999925 | 0.999627 | 0.999177 | 0.998446 | 61 | 512024576 | 9.41188 | 7.51037 | 8.15557 | 6.79108 | 6.69249 | 3.239 | 2.89972 | 2.98712 | 2.58567 | 2.53303 | 1 | 1 |
118791644119204724 | 164.41206831 | 8.9951804 | 164.41207311 | 8.99516407 | 11 | 20.4482 | 0.049955 | 19.9932 | 0.031226 | 10 | 19.4595 | 0.055058 | 19.0636 | 0.034954 | 21 | 19.1387 | 0.009942 | 18.6994 | 0.012918 | 18 | 18.9002 | 0.028131 | 18.4224 | 0.017008 | 16 | 18.7551 | 0.03906 | 18.3453 | 0.048406 | 0.999824 | 0.999525 | 0.999536 | 0.999698 | 0.999067 | 53 | 444915712 | 3.239 | 3.36407 | 3.61128 | 2.58715 | 5.26874 | 1.32149 | 1.37188 | 1.33232 | 1.067 | 1.54819 | 1 | 1 |
118791644639089117 | 164.4639088 | 8.99886558 | 164.4638893 | 8.99882395 | 12 | 20.5845 | 0.013954 | 20.5222 | 0.025129 | 9 | 19.5392 | 0.014182 | 19.4673 | 0.028032 | 25 | 19.2291 | 0.009582 | 19.0912 | 0.010408 | 8 | 18.9844 | 0.019372 | 18.8641 | 0.012195 | 10 | 18.8694 | 0.03332 | 18.7261 | 0.050969 | 0.999824 | 0.999727 | 0.999752 | 0.998658 | 0.99909 | 53 | 444915712 | 1.71052 | 2.17727 | 1.84635 | 1.71799 | 1.60121 | 0.810568 | 0.902419 | 0.786221 | 0.69683 | 0.685771 | 1 | 1 |
112311673903612557 | 167.39036582 | 3.59342292 | 167.39034643 | 3.59342679 | 10 | 20.6191 | 0.028819 | 20.0852 | 0.034735 | 13 | 19.2242 | 0.015424 | 18.6072 | 0.019927 | 30 | 18.6517 | 0.029103 | 18.0381 | 0.010267 | 15 | 18.6967 | 0.024408 | 17.833 | 0.016486 | 15 | 18.2745 | 0.050807 | 17.5922 | 0.029465 | 0.999542 | 0.999752 | 0.999918 | 0.999668 | 0.999759 | 61 | 512024576 | 4.49931 | 5.2592 | 5.923 | 6.45203 | 9.71826 | 1.7136 | 1.89471 | 2.12116 | 2.27382 | 2.50151 | 1 | 1 |
112311673903612557 | 167.39036582 | 3.59342292 | 167.39034643 | 3.59342679 | 10 | 20.6191 | 0.028819 | 20.0852 | 0.034735 | 13 | 19.2242 | 0.015424 | 18.6072 | 0.019927 | 30 | 18.6517 | 0.029103 | 18.0381 | 0.010267 | 15 | 18.6967 | 0.024408 | 17.833 | 0.016486 | 15 | 18.2745 | 0.050807 | 17.5922 | 0.029465 | 0.999542 | 0.999752 | 0.999918 | 0.999668 | 0.999759 | 61 | 512024576 | 5.75997 | 6.37828 | 5.77622 | 5.6901 | 6.93946 | 1.83295 | 2.14678 | 1.96195 | 1.98665 | 1.96887 | 1 | 1 |
112311673927395135 | 167.3927613 | 3.59560997 | 167.39274475 | 3.59561368 | 8 | 21.2923 | 0.075464 | 20.5347 | 0.03857 | 12 | 19.8838 | 0.034 | 19.2746 | 0.020544 | 37 | 19.3558 | 0.023752 | 18.7983 | 0.013654 | 14 | 19.3137 | 0.033713 | 18.5508 | 0.025501 | 12 | 18.9165 | 0.035194 | 18.1912 | 0.03329 | 0.999655 | 0.999603 | 0.99976 | 0.999207 | 0.999066 | 53 | 444915712 | 2.69325 | 3.55384 | 2.87281 | 3.02684 | 139.143 | 0.821187 | 1.09999 | 1.06384 | 1.13938 | -999 | 1 | 1 |
Obtain lightcurves for a given set of objects
Goal/Description
The goal is to obtain light curves for a list of objects. The list of objects with their RA/Dec position is uploaded into Mydb, the PS1 objects are identified, and then the light curves are obtained. Here we upload a list of 13 RR Lyrae stars from the Catalina sky survey into mydb.[RRLcatalina]
Query #1: Match Catalina RR Lyrae stars to PS1 objects
We search for objects in the PS1 DB that are within 3 arcsec of the Catalina positions. Most of the parameters (mean mags, # of detections, etc) we get from the MeanObjectView table. We also join the StackObjectAttributes table in order to get primaryDetection and bestDetection (see XYZ for a more detailed description). These RR Lyrae stars are bright, thus we can expect many detections, and we therefore require o.nDetections>5. (add more about Qfperfect). In order to select only stars (and exclude galaxies), we require (o.rmeanpsfmag - o.rmeankronmag < 0.05). (Should we also make cuts on o.qualityFlag or o.gFlags etc?)
Results (25 minutes)
There are 12 out of 13 matches, and below are the first 10 entries
CSS_J220309.8+051633 | 330.79097 | 5.27593 | 16.68 | 0.638698 | 0.38 | 294 | 15.62 | 0.057 | 1104118064018 | 114333307910211643 | 330.7910125 | 5.2759626 | 0.00217 | 0.00427 | 60 | 17.0665 | 0.003394 | 5 | 16.6873 | 0.002055 | 3 | 16.5998 | 0.01256 | 17 | 16.4921 | 0.018882 | 8 | 16.5424 | 0.012265 | 12 | 16.7216 | 0.007911 | 75 | 12 | 7 | 28 | 11 | 17 | 115000 | 0.999497 | 115000 | 0.999265 | 115000 | 0.999639 | 115000 | 0.999697 | 115000 | 0.998978 | 1 | 1 |
CSS_J221949.2-024020 | 334.95521 | -2.67224 | 16.51 | 0.709491 | 0.37 | 249 | 14.12 | 0.085 | 1001120004060 | 104793349551433447 | 334.95514654 | -2.6725015 | 0.03063 | 0.02043 | 60 | 16.7444 | 0.006159 | 5 | 16.4611 | 0.006758 | 6 | 16.4771 | 0.004479 | 13 | 16.3752 | 0.005638 | 7 | 16.3594 | 0.018196 | 7 | 16.5058 | 0.003776 | 63 | 8 | 11 | 23 | 10 | 11 | 115000 | 0.999646 | 115000 | 0.999825 | 115000 | 0.999672 | 115000 | 0.998727 | 115000 | 0.999331 | 1 | 1 |
CSS_J220559.8-023949 | 331.49951 | -2.66365 | 18.11 | 0.53717 | 0.89 | 234 | 27.82 | 0.152 | 1001118004001 | 104803314996713860 | 331.49966923 | -2.66380567 | 0.00559 | 0.00857 | 52 | 18.6598 | 0.034348 | 7 | 18.4456 | 0.032674 | 8 | 18.0895 | 0.030017 | 19 | 17.9296 | 0.046795 | 10 | 17.9923 | 0.00375 | 9 | 18.4806 | 0.024847 | 67 | 12 | 12 | 21 | 12 | 10 | 115000 | 0.999129 | 115000 | 0.99931 | 115000 | 0.999638 | 115000 | 0.999217 | 115000 | 0.998798 | 1 | 1 |
CSS_J220457.2-051411 | 331.23867 | -5.23656 | 15.64 | 0.53643 | 0.75 | 173 | 9.67 | 0.06 | 1004118010930 | 101713312388786386 | 331.23888082 | -5.23675594 | 0.00469 | 0.00529 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
CSS_J215602.1-052021 | 329.00905 | -5.33921 | 16.51 | 0.757558 | 0.47 | 218 | 14.77 | 0.036 | 1004117008292 | 101593290092623326 | 329.00923968 | -5.33928719 | 0.0167 | 0.01841 | 60 | 16.8494 | 0.0078 | 6 | 16.4217 | 0.022047 | 15 | 16.3759 | 0.008041 | 15 | 16.3468 | 0.003574 | 8 | 16.3721 | 0.018063 | 11 | 16.4827 | 0.023415 | 72 | 10 | 16 | 22 | 12 | 12 | 115000 | 0.999921 | 115000 | 0.999677 | 115000 | 0.999406 | 115000 | 0.999167 | 115000 | 0.999435 | 1 | 1 |
CSS_J220820.9-020222 | 332.08714 | -2.0397 | 17.01 | 0.561687 | 0.95 | 263 | 17.61 | 0.092 | 1001119018133 | 105553320871392714 | 332.08715134 | -2.03979472 | 0.0027 | 0.0102 | 60 | 17.7555 | 0.003879 | 8 | 17.382 | 0.008932 | 13 | 17.1634 | 0.03364 | 23 | 17.1783 | 0.007448 | 4 | 16.9441 | 0.013463 | 4 | 17.3846 | 0.009404 | 80 | 14 | 21 | 29 | 8 | 8 | 115000 | 0.999685 | 115000 | 0.999274 | 115000 | 0.999878 | 115000 | 0.998871 | 115000 | 0.999672 | 1 | 1 |
CSS_J222707.9+051259 | 336.78311 | 5.21646 | 16.19 | 0.552557 | 0.73 | 275 | 11.29 | 0.173 | 1104120055941 | 114263367831840264 | 336.78319546 | 5.21647999 | 0.00177 | 0.00188 | 60 | 16.3358 | 0.006233 | 6 | 16.2293 | 0.117566 | 7 | 16.2794 | 0.016904 | 25 | 16.1658 | 0.024596 | 9 | 16.084 | 0.028519 | 9 | 16.2807 | 0.134897 | 87 | 13 | 15 | 36 | 12 | 11 | 115000 | 0.999558 | 115000 | 0.99899 | 115000 | 0.999677 | 115000 | 0.999277 | 115000 | 0.998572 | 1 | 1 |
CSS_J215435.8-005450 | 328.64951 | -0.91416 | 17.75 | 0.575212 | 0.9 | 257 | 24.14 | 0.126 | 1001117050169 | 106903286496013281 | 328.64959053 | -0.91431682 | 0.00219 | 0.00138 | 52 | 18.1409 | 0.074842 | 7 | 17.8706 | 0.057256 | 10 | 17.6159 | 0.042086 | 17 | 17.7657 | 0.024178 | 9 | 17.669 | 0.011157 | 10 | 17.9065 | 0.062371 | 75 | 12 | 12 | 26 | 13 | 12 | 115000 | 0.999682 | 115000 | 0.999313 | 115000 | 0.999291 | 115000 | 0.999697 | 115000 | 0.999024 | 1 | 1 |
CSS_J222942.0-040519 | 337.42517 | -4.08876 | 18.08 | 0.475699 | 1.19 | 230 | 29.63 | 0.067 | 1004120034845 | 103093374253263940 | 337.42533413 | -4.08873128 | 0.00245 | 0.00194 | 52 | 18.838 | 0.022485 | 9 | 18.552 | 0.01008 | 8 | 18.5071 | 0.013825 | 14 | 18.2355 | 0.118849 | 8 | 18.3454 | 0.019402 | 10 | 18.6314 | 0.011776 | 82 | 14 | 16 | 26 | 12 | 14 | 115000 | 0.999522 | 115000 | 0.999581 | 115000 | 0.999479 | 115000 | 0.999669 | 115000 | 0.99935 | 1 | 1 |
CSS_J223105.2+000538 | 337.77177 | 0.09404 | 16.97 | 0.670228 | 0.4 | 278 | 17.7 | 0.071 | 1101121002733 | 108113377718393164 | 337.77185997 | 0.09391985 | 0.01024 | 0.01061 | 60 | 17.1952 | 0.013075 | 7 | 16.9806 | 0.006582 | 14 | 16.8766 | 0.012872 | 30 | 16.8465 | 0.015577 | 8 | 16.7479 | 0.011255 | 9 | 17.0236 | 0.005972 | 94 | 14 | 22 | 35 | 10 | 13 | 115000 | 0.999502 | 115000 | 0.999683 | 115000 | 0.999733 | 115000 | 0.999191 | 115000 | 0.999694 | 1 | 1 |
Query #2: Get the detections
Now we get all detections associated with these objIDs
Results (30 seconds)
There are 946 detection entries for the 12 objects, and below are the first 10 entries
101713312388786386 | 331.23888082 | -5.23675594 | 331.23887966 | -5.23673709 | 0.0142129 | 0.0204369 | 281041231200000230 | 56911.4125766 | 45 | 1.13479 | 0.00236808 | 3.23403E-05 | 0.0749382 | 0.0282996 | 0.001481 | 1.22177 | 3.17838 | 24.5609 | 0.000101192 | 1.15941E-06 | 79216420 | 3 | 7.38736E-05 | 3.65002E-06 | 102760453 | 128 | 32768 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.2388835 | -5.2367367 | 0.00872209 | 0.0136836 | 281040069200000148 | 56911.4009661 | 45 | 1.12094 | 0.00235791 | 2.46269E-05 | 0.115458 | 0.115458 | -0.683352 | 1.38811 | -0.407894 | 24.5606 | 0.000115057 | 1.24099E-06 | 79214720 | 3 | 6.59469E-05 | 3.4392E-06 | 102760453 | 128 | 32768 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23887945 | -5.23675267 | 0.00255273 | 0.00272007 | 272155268140000054 | 56822.5529494 | 45 | 1.23978 | 0.00160025 | 5.42303E-06 | 0.998273 | 0.998273 | 0.718458 | 1.85151 | 0.36052 | 24.6031 | 0.00163074 | 4.55078E-06 | 75434414 | 3 | 9.63136E-05 | 4.39628E-06 | 102760453 | 128 | 7374912 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23887517 | -5.23675357 | 0.00255573 | 0.00279838 | 272154101140000055 | 56822.5412889 | 45 | 1.29101 | 0.00163992 | 5.55706E-06 | 0.999095 | 0.999095 | 0.577144 | 1.65577 | 0.557561 | 24.6016 | 0.00165362 | 4.55156E-06 | 75432714 | 3 | 0.000102506 | 4.54961E-06 | 102760517 | 128 | 7374912 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23888039 | -5.23675471 | 0.00251159 | 0.00252325 | 272156434140000061 | 56822.5646058 | 45 | 1.19898 | 0.001599 | 5.33245E-06 | 0.999192 | 0.999192 | 0.162726 | 1.95035 | 1.39596 | 24.6048 | 0.00165668 | 4.56818E-06 | 75436114 | 3 | 9.17713E-05 | 4.18141E-06 | 102760453 | 128 | 7374912 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23888187 | -5.23675805 | 0.00228002 | 0.00229566 | 272157596140000058 | 56822.576232 | 45 | 1.16719 | 0.00160726 | 5.26372E-06 | 0.999501 | 0.999501 | 0.222933 | 1.89115 | 1.21877 | 24.6047 | 0.00165819 | 4.55018E-06 | 75437814 | 3 | 8.90853E-05 | 4.19146E-06 | 102760517 | 128 | 7374912 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23887668 | -5.23673914 | 0.0022578 | 0.0019803 | 273861056470000149 | 56839.6107502 | 30 | 1.13731 | 0.00197136 | 8.18044E-06 | 0.885171 | 0 | -0.226784 | 1.2275 | -1.20868 | 24.2217 | 0.00172437 | 6.86483E-06 | 76384947 | 4 | 5.24721E-05 | 5.20364E-06 | 102760453 | 160 | 34880 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23888109 | -5.23674994 | 0.00334748 | 0.00308122 | 274160857470000086 | 56842.6087543 | 30 | 1.148 | 0.00144126 | 7.36061E-06 | 0.934025 | 0 | -0.358647 | 1.03449 | -0.917947 | 24.2402 | 0.00138292 | 6.01342E-06 | 76561547 | 4 | 6.60553E-05 | 5.55748E-06 | 102760517 | 160 | 34880 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23887875 | -5.23674419 | 0.00621135 | 0.00628081 | 284035135370000088 | 56941.351618 | 45 | 1.17746 | 0.00125442 | 5.51837E-06 | 0.999367 | 0.999367 | 0.0518508 | 1.20217 | 1.94437 | 24.5348 | 0.00127698 | 4.00209E-06 | 80756237 | 3 | 5.62479E-05 | 3.33006E-06 | 102760453 | 128 | 7342144 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 331.23887939 | -5.2367617 | 0.00649579 | 0.00662178 | 284036359370000086 | 56941.3638651 | 45 | 1.21437 | 0.00125844 | 5.59236E-06 | 0.99913 | 0.99913 | 0.0215391 | 2.1285 | 2.2984 | 24.5234 | 0.00114858 | 3.81474E-06 | 80758037 | 3 | 5.97269E-05 | 3.37162E-06 | 102760453 | 128 | 7374912 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
Query #3: Get the forced detections
Now we get the forced photometry
Results (3:45 minutes)
There are 824 forced detection entries for the 12 objects, and below are the first 10 entries
101713312388786386 | 331.23888082 | -5.23675594 | 2473948760665753129 | 55509.1963096 | 30 | 1.12528 | 0.00226155 | 8.89297E-06 | 0.998447 | 0.899065 | 1.70509 | 24.2184 | 0.00225853 | 7.8829E-06 | 57306884 | 4 | 2.44443E-07 | 5.58402E-06 | 169869889 | 0 | 124781568 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2473949315790276137 | 55509.2069606 | 30 | 1.11611 | 0.00237623 | 8.73044E-06 | 0.997987 | 0.619015 | 1.98874 | 24.2053 | 0.00234477 | 8.07563E-06 | 57306886 | 4 | -7.89231E-08 | 4.77904E-06 | 706740801 | 8 | 0 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2473960382578820649 | 56084.5997338 | 30 | 1.16011 | 0.00154027 | 7.7769E-06 | 0.998149 | 0.998149 | 1.79974 | 24.2926 | 0.00168208 | 6.49405E-06 | 57306888 | 4 | 1.73045E-07 | 6.09569E-06 | 169869889 | 0 | 7341056 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2473961698449425961 | 56439.5801154 | 60 | 1.34319 | 0.00156916 | 9.80598E-06 | 0.272597 | 0.180329 | 1.17526 | 24.2592 | 0.000423637 | 2.32656E-06 | 57306890 | 4 | -1.71004E-07 | 3.96178E-06 | 169869889 | 0 | 0 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2473962046341776937 | 56595.2046749 | 60 | 1.15427 | 0.00151238 | 4.94983E-06 | 0.998573 | 0.998573 | 3.21986 | 24.2944 | 0.00155572 | 4.46429E-06 | 57306893 | 4 | 8.98555E-08 | 3.09952E-06 | 169869889 | 0 | 40895488 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2473962214919243305 | 56084.5880277 | 30 | 1.19001 | 0.00150664 | 7.84793E-06 | 0.998277 | 0.998277 | 1.7081 | 24.2845 | 0.00161956 | 6.38274E-06 | 57306894 | 4 | 3.2375E-07 | 6.02042E-06 | 169869889 | 0 | 7341056 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2474105536904169001 | 55451.3857987 | 40 | 1.11324 | 0.000895305 | 6.75477E-06 | 0.725763 | 0.559601 | 71.772 | 24.69 | 0.00205695 | 5.07366E-06 | 57306901 | 2 | 6.39024E-09 | 1.49626E-06 | 169869889 | 0 | 0 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2474105805339625001 | 55801.4656768 | 40 | 1.16095 | 0.000616471 | 4.43598E-06 | 0.998961 | 0.998961 | 63.0533 | 24.6782 | 0.00206805 | 5.10856E-06 | 57306905 | 2 | 5.8963E-08 | 1.63808E-06 | 169869889 | 0 | 7341056 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2474105863321683497 | 55801.4755793 | 40 | 1.18592 | 0.001098 | 4.21394E-06 | 0.998708 | 0.998708 | 2.32906 | 24.6883 | 0.001268 | 4.01415E-06 | 57306906 | 2 | 1.10105E-07 | 1.71416E-06 | 169869889 | 0 | 108004352 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
101713312388786386 | 331.23888082 | -5.23675594 | 2474105881038423593 | 55804.378647 | 40 | 1.13812 | 0.00159737 | 7.25792E-06 | 0.613981 | 0.505588 | 12.0862 | 24.692 | 0.00101107 | 3.5996E-06 | 57306907 | 2 | 7.46216E-08 | 1.61881E-06 | 169869889 | 0 | 0 | 60 | 16.2717 | 0.027581 | 6 | 15.5956 | 0.03394 | 6 | 15.8879 | 0.034506 | 12 | 15.8923 | 0.017074 | 3 | 15.8542 | 0.007946 | 5 | 15.7368 | 0.076347 | 73 | 16 | 14 | 24 | 10 | 9 | 115000 | 0.999681 | 115000 | 0.999384 | 115000 | 0.999501 | 115000 | 0.998761 | 115000 | 0.997996 | 1 | 1 |
Results
1097 objects, here are the first 10:
...
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
Code Block | ||
---|---|---|
| ||
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.[C14] 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 > 0.05)
|
Results
First 10 Rows on MyDB Table K2C14
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Sample Query 1 template (this is a template, just cut and paste, do not modify)
Goal/Description
fill in
Query
fill in
Results
...
...