The VIMOS Public Extragalactic Redshift Survey (VIPERS)

A support vector machine classification of galaxies, stars, and AGNs^⋆

¹ Department of Particle and Astrophysical Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8602, Nagoya, Japan
e-mail: malek.kasia@nagoya-u.jp
² Astronomical Observatory of the Jagiellonian University, Orla 171, 30-001 Cracow, Poland
³ National Centre for Nuclear Research, ul. Hoza 69, 00-681 Warszawa, Poland
⁴ INAF – Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via Bassini 15, 20133 Milano, Italy
⁵ Aix Marseille Université, CNRS, LAM (Laboratoire d’Astrophysique de Marseille) UMR 7326, 13388 Marseille, France
⁶ INAF – Osservatorio Astronomico di Brera, via Brera 28, 20122 Milano, via E. Bianchi 46, 23807 Merate, Italy
⁷ INAF – Osservatorio Astrofisico di Torino, 10025 Pino Torinese, Italy
⁸ Canada-France-Hawaii Telescope, 65–1238 Mamalahoa Highway, Kamuela, HI 96743, USA
⁹ Aix-Marseille Université, CNRS, CPT (Centre de Physique Théorique) UMR 7332, 13288 Marseille, France
¹⁰ INAF – Osservatorio Astronomico di Bologna, via Ranzani 1, 40127 Bologna, Italy
¹¹ Dipartimento di Matematica e Fisica, Università degli Studi Roma Tre, via della Vasca Navale 84, 00146 Roma, Italy
¹² INFN, Sezione di Roma Tre, via della Vasca Navale 84, 00146 Roma, Italy
¹³ INAF – Osservatorio Astronomico di Roma, via Frascati 33, 00040 Monte Porzio Catone (RM), Italy
¹⁴ Laboratoire Lagrange, UMR7293, Université de Nice Sophia-Antipolis, CNRS, Observatoire de la Côte d’Azur, 06300 Nice, France
¹⁵ Institute of Astronomy and Astrophysics, Academia Sinica, PO Box 23-141, Taipei 10617, Taiwan
¹⁶ Dipartimento di Fisica e Astronomia – Università di Bologna, viale Berti Pichat 6/2, 40127 Bologna, Italy
¹⁷ INAF – Osservatorio Astronomico di Trieste, via G. B. Tiepolo 11, 34143 Trieste, Italy
¹⁸ SUPA, Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
¹⁹ Dipartimento di Fisica, Università di Milano-Bicocca, P.zza della Scienza 3, 20126 Milano, Italy
²⁰ Institute of Physics, Jan Kochanowski University, ul. Swietokrzyska 15, 25-406 Kielce, Poland
²¹ INFN, Sezione di Bologna, viale Berti Pichat 6/2, 40127 Bologna, Italy
²² Institute d’Astrophysique de Paris, UMR7095 CNRS, Université Pierre et Marie Curie, 98bis Boulevard Arago, 75014 Paris, France
²³ Universitätssternwarte München, Ludwig-Maximillians Universität, Scheinerstr. 1, 81679 München, Germany
²⁴ Max-Planck-Institut für Extraterrestrische Physik, 84571 Garching b. München, Germany
²⁵ Institute of Cosmology and Gravitation, Dennis Sciama Building, University of Portsmouth, Burnaby Road, Portsmouth, PO1 3FX, UK
²⁶ INAF – Istituto di Astrofisica Spaziale e Fisica Cosmica Bologna, via Gobetti 101, 40129 Bologna, Italy
²⁷ INAF – Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy
²⁸ Università degli Studi di Milano, via G. Celoria 16, 20130 Milano, Italy

Received: 11 March 2013
Accepted: 6 June 2013

Abstract

Aims. The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and to apply the techniques to the VIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u^∗,g′,r′,i′) and near-infrared (NIR) data (z′, K_s), we develop a classifier, based on broad-band photometry, for identifying stars, active galactic nuclei (AGNs), and galaxies, thereby improving the purity of the VIPERS sample.

Methods. Support vector machine (SVM) supervised learning algorithms allow the automatic classification of objects into two or more classes based on a multidimensional parameter space. In this work, we tailored the SVM to classifying stars, AGNs, and galaxies and applied this classification to the VIPERS data. We trained the SVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys.

Results. We tested two SVM classifiers and concluded that including NIR data can significantly improve the efficiency of the classifier. The self-check of the best optical + NIR classifier has shown 97% accuracy in the classification of galaxies, 97% for stars, and 95% for AGNs in the 5-dimensional colour space. In the test of VIPERS sources with 99% redshift confidence, the classifier gives an accuracy equal to 94% for galaxies, 93% for stars, and 82% for AGNs. The method was applied to sources with low-quality spectra to verify their classification, hence increasing the security of measurements for almost 4900 objects.

Conclusions. We conclude that the SVM algorithm trained on a carefully selected sample of galaxies, AGNs, and stars outperforms simple colour–colour selection methods and can be regarded as a very efficient classification method particularly suitable for modern large surveys.