Volume 617, September 2018
|Number of page(s)||25|
|Published online||21 September 2018|
The VIMOS Public Extragalactic Redshift Survey (VIPERS)
The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms⋆
Center for Theoretical Physics, Al. Lotnikow 32/46, 02-668 Warsaw, Poland
2 National Centre for Nuclear Research, ul. Hoza 69, 00-681 Warszawa, Poland
3 Astronomical Observatory of the Jagiellonian University, Orla 171, 30-001 Cracow, Poland
4 INAF – Osservatorio Astronomico di Brera, Via Brera 28, 20122 Milano – via E. Bianchi 46, 23807 Merate, Italy
5 INAF – Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via Bassini 15, 20133 Milano, Italy
6 Department of Astronomy & Physics, Saint Mary’s University, 923 Robie Street, Halifax, Nova Scotia, B3H 3C3 Canada
7 Aix-Marseille Université, CNRS, LAM, Laboratoire d’Astrophy-sique de Marseille, Marseille, France
8 INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna, via Gobetti 93/3, 40129 Bologna, Italy
9 Università degli Studi di Milano, via G. Celoria 16, 20133 Milano, Italy
10 INAF - Osservatorio Astrofisico di Torino, 10025 Pino Torinese, Italy
11 Laboratoire Lagrange, UMR7293, Université de Nice Sophia Antipolis, CNRS, Observatoire de la Côte d’Azur, 06300 Nice, France
12 Dipartimento di Fisica e Astronomia - Alma Mater Studiorum Università di Bologna, via Gobetti 93/2, 40129 Bologna, Italy
13 Institute of Physics, Jan Kochanowski University, ul. Swietokrzyska 15, 25-406 Kielce, Poland
14 INFN, Sezione di Bologna, viale Berti Pichat 6/2, 40127 Bologna, Italy
15 IRAP, Université de Toulouse, CNRS, UPS, Toulouse, France
16 IRAP, 9 av. du colonel Roche, BP 44346, 31028 Toulouse Cedex 4, France
17 School of Physics and Astronomy, University of St Andrews, St Andrews, KY16 9SS UK
18 INAF – Istituto di Radioastronomia, via Gobetti 101, 40129 Bologna, Italy
19 Canada–France–Hawaii Telescope, 65–1238 Mamalahoa Highway, Kamuela, HI, 96743 USA
20 Aix-Marseille Univ., Univ. Toulon CNRS, CPT Fsect, Marseille, France
21 Dipartimento di Matematica e Fisica, Università degli Studi Roma Tre, via della Vasca Navale 84, 00146 Roma, Italy
22 INFN, Sezione di Roma Tre, via della Vasca Navale 84, 00146 Roma, Italy
23 INAF - Osservatorio Astronomico di Roma, via Frascati 33, 00040 Monte Porzio Catone (RM), Italy
24 Department of Astronomy, University of Geneva, Ch. d’Ecogia 16, 1290 Versoix, Switzerland
25 INAF - Osservatorio Astronomico di Trieste, via G. B. Tiepolo 11, 34143 Trieste, Italy
26 Division of Particle and Astrophysical Science, Nagoya University, Furo-cho, Chikusa-ku, 464-8602 Nagoya, Japan
Accepted: 30 May 2018
Aims. Various galaxy classification schemes have been developed so far to constrain the main physical processes regulating evolution of different galaxy types. In the era of a deluge of astrophysical information and recent progress in machine learning, a new approach to galaxy classification has become imperative.
Methods. In this paper, we employ a Fisher Expectation-Maximization (FEM) unsupervised algorithm working in a parameter space of 12 rest-frame magnitudes and spectroscopic redshift. The model (DBk) and the number of classes (12) were established based on the joint analysis of standard statistical criteria and confirmed by the analysis of the galaxy distribution with respect to a number of classes and their properties. This new approach allows us to classify galaxies based on only their redshifts and ultraviolet to near-infrared (UV–NIR) spectral energy distributions.
Results. The FEM unsupervised algorithm has automatically distinguished 12 classes: 11 classes of VIPERS galaxies and an additional class of broad-line active galactic nuclei (AGNs). After a first broad division into blue, green, and red categories, we obtained a further sub-division into: three red, three green, and five blue galaxy classes. The FEM classes follow the galaxy sequence from the earliest to the latest types, which is reflected in their colours (which are constructed from rest-frame magnitudes used in the classification procedure) but also their morphological, physical, and spectroscopic properties (not included in the classification scheme). We demonstrate that the members of each class share similar physical and spectral properties. In particular, we are able to find three different classes of red passive galaxy populations. Thus, we demonstrate the potential of an unsupervised approach to galaxy classification and we retrieve the complexity of galaxy populations at z ∼ 0.7, a task that usual, simpler, colour-based approaches cannot fulfil.
Key words: galaxies: evolution / galaxies: star formation / galaxies: stellar content
Based on observations collected at the European Southern Observatory, Cerro Paranal, Chile, using the Very Large Telescope under programs 182.A–0886 and partly 070.A–9007. Also based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada–France–Hawaii Telescope (CFHT), which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is based in part on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the Canada–France–Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. The VIPERS web site is http://www.vipers.inaf.it/
© ESO 2018
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