Active galactic nuclei catalog from the AKARI NEP-Wide field^⋆

¹ National Centre for Nuclear Research, Pasteura 7, 093 Warsaw, Poland
e-mail: artem.poliszczuk@ncbj.gov.pl
² Astronomical Observatory of the Jagiellonian University, ul.Orla 171, 244 Krakow, Poland
³ Aix Marseille Univ. CNRS, CNES, LAM, Marseille, France
⁴ Institute of Astronomy, National Tsing Hua University, 101, Section 2. Kuang-Fu Road, Hsinchu 30013, Taiwan
⁵ Department of physics and astronomy, UCLA, 475 Portola Plaza, Los Angeles, CA 90095-1547, USA
⁶ Tokyo University of Science, 1-3, Kagurazaka, Shinjuku, Tokyo 8601, Japan
⁷ Department of Earth Science Education, Kyungpook National University, Daegu 41566, Korea
⁸ RAL Space, Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire OX11 0QX, UK
⁹ School of Physical Sciences, The Open University, Milton Keynes MK7 6AA, UK
¹⁰ Oxford Astrophysics, University of Oxford, Keble Rd, Oxford OX1 3RH, UK
¹¹ Astronomy Program, Department of Physics and Astronomy, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
¹² Department of Astronomy, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 8502, Japan
¹³ Academia Sinica Institute of Astronomy and Astrophysics, 11F of Astronomy-Mathematics Building, AS/NTU, No.1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
¹⁴ Research Center for Space and Cosmic Evolution, Ehime University, 2-5 Bunkyo-cho, Matsuyama, Ehime 8577, Japan
¹⁵ Korea Astronomy and Space Science Institute (KASI), 776 Daedeok-daero, Yuseong-gu, Daejeon 34055, Korea

Received: 23 December 2020
Accepted: 13 April 2021

Abstract

Context. The north ecliptic pole (NEP) field provides a unique set of panchromatic data that are well suited for active galactic nuclei (AGN) studies. The selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such methods, despite their effectiveness, strongly reduce the breadth of resulting catalogs due to the MIR detection condition. Modern machine learning techniques can solve this problem by finding similar selection criteria using only optical and near-infrared (NIR) data.

Aims. The aim of this study is to create a reliable AGN candidates catalog from the NEP field using a combination of optical SUBARU/HSC and NIR AKARI/IRC data and, consequently, to develop an efficient alternative for the MIR-based AKARI/IRC selection technique.

Methods. We tested set of supervised machine learning algorithms for the purposes of carrying out an efficient process for AGN selection. The best models were compiled into a majority voting scheme, which used the most popular classification results to produce the final AGN catalog. An additional analysis of the catalog properties was performed as a spectral energy distribution fitting via the CIGALE software.

Results. The obtained catalog of 465 AGN candidates (out of 33 119 objects) is characterized by 73% purity and 64% completeness. This new classification demonstrates a suitable consistency with the MIR-based selection. Moreover, 76% of the obtained catalog can be found solely using the new method due to the lack of MIR detection for most of the new AGN candidates. The training data, codes, and final catalog are available via the github repository. The final catalog of AGN candidates is also available via the CDS service.

Conclusions. The new selection methods presented in this paper are proven to be a better alternative for the MIR color AGN selection. Machine learning techniques not only show similar effectiveness, but also involve less demanding optical and NIR observations, substantially increasing the extent of available data samples.