Photometric Classification of quasars from RCS-2 using Random Forest

Abstract

Aims. Construction of 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. Methods. 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 colours. 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. Results. 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; 6,530 quasar candidates are identified from 16,898 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 21,834 quasar candidates from 242,902 sources. We compile 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. Conclusions. 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.

Accepted for publication in A&A. 20 pages, 16 figures, 6 tables. Tables 1, 2 and 3 with the quasar candidates are only available in electronic format http://www.aanda.org/

References in corpus (10)

• Think Outside the Color Box: Probabilistic Target Selection and the SDSS-XDQSO Quasar Targeting Catalog
• The FIRST-2MASS Red Quasar Survey
• A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
• MegaZ-LRG: A photometric redshift catalogue of one million SDSS Luminous Red Galaxies
• Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees
• The Sloan Digital Sky Survey Quasar Lens Search. III. Constraints on Dark Energy from the Third Data Release Quasar Lens Catalog
• Galaxy Clusters in the Line of Sight to Background Quasars: I. Survey Design and Incidence of MgII Absorbers at Cluster Redshifts
• The UV Properties of SDSS Selected Quasars
• Automatic Classification of Variable Stars in Catalogs with missing data
• Redshifts and lens profile for the double quasar QJ 0158-4325

Cited by in corpus (10)

• Catalogues of Active Galactic Nuclei From Gaia and unWISE Data
• Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys
• On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2
• Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4
• XMMPZCAT: A catalogue of photometric redshifts for X-ray sources
• Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS
• Identifying Tidal Disruption Events via Prior Photometric Selection of Their Preferred Hosts
• The Probabilistic Random Forest applied to the selection of quasar candidates in the QUBRICS Survey
• Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms
• Photometric classification of QSOs from ALHAMBRA survey using random forest