Photometric classification of quasars from RCS-2 using Random Forest^⋆

¹ Instituto de Astrofísica, Pontificia Universidad Católica de Chile, 4860 Avenida Vicuña Mackenna, Santiago, Chile
e-mail: dcarrasco@student.unimelb.edu.au
² School of Physics, University of Melbourne, Parkville, Victoria, VIC3010 Australia
³ Millennium Institute of Astrophysics, Chile
⁴ Departamento de Ciencia de la Computación, Pontificia Universidad Católica de Chile, 4860 Avenida Vicuña Mackenna, Santiago, Chile
⁵ Departamento de Ciencias Físicas, Universidad Andres Bello, 252 Avenida República, Santiago, Chile
⁶ South African Astronomical Observatory, PO Box 9, 7935 Observatory, South Africa
⁷ Kavli Institute for Cosmological Physics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA
⁸ Department of Astronomy and Astrophysics, University of Chicago, 5640 South Ellis Avenue, Chicago, IL 60637, USA
⁹ Department of Astronomy and Astrophysics, University of Toronto, 60 St. George Street, Toronto, Ontario M5S 3H8, Canada
¹⁰ Institute of Astrophysics & Astronomy, Academia Sinica, 106 Taipei, Taiwan, R.O.C.
¹¹ Departamento de Astronomía, Universidad de Chile, Casilla 36-D, Santiago, Chile

Received: 27 January 2015
Accepted: 24 August 2015

Abstract

The classification and identification of quasars is fundamental to many astronomical research areas. Given the large volume of photometric survey data available in the near future, automated methods for doing so are required. In this article, we present a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested using a well-defined SDSS spectroscopic sample of quasars and stars. The Random Forest algorithm constructs the catalog from RCS-2 point sources using SDSS spectroscopically-confirmed stars and quasars. 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 (the fraction of true positives within the group assigned quasar status) of 89.5% and recall (the fraction of true positives relative to all sources that actually are quasars) of 88.4%. These performance metrics improve for the GALEX subset: 6529 quasar candidates are identified from 16 898 sources, with a precision and recall of 97.0% and 97.5%, respectively. Algorithm performance is further improved when WISE data are included, with precision and recall increasing to 99.3% and 99.1%, respectively, for 21 834 quasar candidates from 242 902 sources. We compiled our final catalog (38 257) by merging these samples and removing duplicates. An observational follow up of 17 bright (r < 19) candidates with long-slit spectroscopy at DuPont telescope (LCO) yields 14 confirmed quasars. The results signal encouraging progress in the classification of point sources with Random Forest algorithms to search for quasars within current and future large-area photometric surveys.