Star-galaxy separation in the AKARI NEP deep field

¹ Department of Particle and Astrophysical Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8602 Nagoya, Japan
e-mail: solarz.aleksandra.alicja@c.mbox.nagoya-u.ac.jp
² The Astronomical Observatory of the Jagiellonian University, ul. Orla 171, 30-244 Kraków, Poland
³ Center for Theoretical Physics of the Polish Academy of Sciences, al. Lotników, 32/46, 02-668 Warsaw, Poland
⁴ The Andrzej Sołtan Institute for Nuclear Studies, ul. Hoża 69, 00-681 Warsaw, Poland
⁵ Institute for Advanced Research, Nagoya University, Furo-cho, Chikusa-ku, 464-8601 Nagoya, Japan
⁶ Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, Sagamihara, 252-5210 Kanagawa, Japan
⁷ Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI, 96822, USA
⁸ National Astronomical Observatory, 2-21-1 Osawa, Mitaka, 181-8588 Tokyo, Japan
⁹ Academia Sinica, Institute of Astronomy and Astrophysics, Taiwan
¹⁰ Space Science and Technology Department, CCLRC Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire, OX11 0QX, UK
¹¹ Department of Physics, University of Lethbridge, 4401 University Drive, Lethbridge, Alberta T1J 1B1, Canada
¹² Astrophysics Group, Department of Physics, The Open University, Milton Keynes, MK7 6AA, UK
¹³ Physics Section, Faculty of Humanities and Social Sciences, Iwate University, 020-8550 Morioka, Japan

Received: 16 September 2011
Accepted: 23 February 2012

Abstract

Context. It is crucial to develop a method for classifying objects detected in deep surveys at infrared wavelengths. We specifically need a method to separate galaxies from stars using only the infrared information to study the properties of galaxies, e.g., to estimate the angular correlation function, without introducing any additional bias.

Aims. We aim to separate stars and galaxies in the data from the AKARI north ecliptic pole (NEP) deep survey collected in nine AKARI/IRC bands from 2 to 24 μm that cover the near- and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the correlation function for NIR and MIR galaxies from a sample selected according to our criteria in future research.

Methods. We used support vector machines (SVM) to study the distribution of stars and galaxies in the AKARIs multicolor space. We defined the training samples of these objects by calculating their infrared stellarity parameter (sgc). We created the most efficient classifier and then tested it on the whole sample. We confirmed the developed separation with auxiliary optical data obtained by the Subaru telescope and by creating Euclidean normalized number count plots.

Results. We obtain a 90% accuracy in pinpointing galaxies and 98% accuracy for stars in infrared multicolor space with the infrared SVM classifier. The source counts and comparison with the optical data (with a consistency of 65% for selecting stars and 96% for galaxies) confirm that our star/galaxy separation methods are reliable.

Conclusions. The infrared classifier derived with the SVM method based on infrared sgc – selected training samples proves to be very efficient and accurate in selecting stars and galaxies in deep surveys at infrared wavelengths carried out without any previous target object selection.