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Description
We present new quasar candidate catalogs from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using a Random Forest algorithm. The algorithm is trained using a well-defined SDSS spectroscopic sample of quasars and stars. The algorithm identifies putative quasars from broadband magnitudes (g, r, i, z) and colors. Exploiting NUV GALEX measurements for a subset of the objects, we refine the classifier by adding new information. An additional subset of the data with WISE W1 and W2 bands is also studied. Upon analyzing 542,897 RCS-2 point sources, the algorithm identified 21,501 quasar candidates, with a training-set-derived precision of 89.5% and recall of 88.4%. These performance metrics improve for the GALEX subset; 6530 quasar candidates are identified from 16898 sources, with a precision and recall respectively of 97.0% and 97.5%. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to 99.3% and 99.1% respectively for 21834 quasar candidates from 242902 sources. After merging these samples and removing duplicates, we obtain 38257 candidates.
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