Photometric classification of quasars from RCS-2 using Random ForestReport as inadecuate

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The classification and identification of quasars is fundamental to many astronomical research areas. Given the large volume of photometricsurvey data available in the near future, automated methods for doing so are required. In this article, we present a newquasar candidate catalog from the Red-Sequence Cluster Survey 2 RCS-2, identified solely from photometric information using anautomated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sampleof quasars and stars. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopicallyconfirmedstars and quasars. The algorithm identifies putative quasars from broadband magnitudes g, r, i, z and colors. ExploitingNUV GALEX measurements for a subset of the objects, we refine the classifier by adding new information. An additional subsetof the data with WISE W1 and W2 bands is also studied. Upon analyzing 542 897 RCS-2 point sources, the algorithm identified21 501 quasar candidates with a training-set-derived precision the fraction of true positives within the group assigned quasar statusof 89.5% and recall the fraction of true positives relative to all sources that actually are quasars of 88.4%. These performance metricsimprove for the GALEX subset: 6529 quasar candidates are identified from 16 898 sources, with a precision and recall of 97.0% and97.5%, respectively. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to99.3% and 99.1%, respectively, for 21 834 quasar candidates from 242 902 sources. We compiled our final catalog 38 257 by mergingthese samples and removing duplicates. An observational follow up of 17 bright r < 19 candidates with long-slit spectroscopyat DuPont telescope LCO yields 14 confirmed quasars. The results signal encouraging progress in the classification of point sourceswith Random Forest algorithms to search for quasars within current and future large-area photometric surveys.Nota general

Artículo de publicación ISI

Author: Carrasco, D.; - Barrientos, L.; - Pichara, K.; - Anguita, T.; - Murphy, D.; - Gilbank, D.; - Gladders, M.; - Yee, H.; - Hsieh, B.



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