2018A&A...617A..70S


C.D.S. - SIMBAD4 rel 1.7 - 2020.07.08CEST04:21:16

2018A&A...617A..70S - Astronomy and Astrophysics, volume 617A, 70-70 (2018/9-1)

The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms.

SIUDEK M., MALEK K., POLLO A., KRAKOWSKI T., IOVINO A., SCODEGGIO M., MOUTARD T., ZAMORANI G., GUZZO L., GARILLI B., GRANETT B.R., BOLZONELLA M., DE LA TORRE S., ABBAS U., ADAMI C., BOTTINI D., CAPPI A., CUCCIATI O., DAVIDZON I., FRANZETTI P., FRITZ A., KRYWULT J., LE BRUN V., LE FEVRE O., MACCAGNI D., MARULLI F., POLLETTA M., TASCA L.A.M., TOJEIRO R., VERGANI D., ZANICHELLI A., ARNOUTS S., BEL J., BRANCHINI E., COUPON J., DE LUCIA G., ILBERT O., HAINES C.P., MOSCARDINI L. and TAKEUCHI T.T.

Abstract (from CDS):


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.

Abstract Copyright: © ESO 2018

Journal keyword(s): galaxies: evolution - galaxies: star formation - galaxies: stellar content

Simbad objects: 9

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Number of rows : 9

N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2020
#notes
1 3C 84 Sy2 03 19 48.1597607660 +41 30 42.114155434   13.10 12.48 11.09   ~ 3544 3
2 NGC 3077 GiP 10 03 19.0965510921 +68 44 01.556166166 11.23 10.85 10.14 9.74   ~ 691 0
3 NGC 3227 GiP 10 23 30.57 +19 51 54.3   12.61 11.79     ~ 1530 2
4 NGC 3327 Sy2 10 39 57.9323601431 +24 05 28.420785736   14.2       ~ 36 0
5 NGC 3353 AGN 10 45 22.390 +55 57 37.36 12.90 13.25 12.79     ~ 319 1
6 M 96 GiP 10 46 45.744 +11 49 11.78 10.42 10.15 9.25 8.99   ~ 741 1
7 M 105 LIN 10 47 49.600 +12 34 53.87   10.56 9.76 9.12 8.18 ~ 1379 0
8 UGC 6697 GiG 11 43 49.078 +19 58 06.46 13.96 14.35 13.59 13.29   ~ 197 2
9 NGC 4775 GiG 12 53 45.707 -06 37 19.73   12.3   11.19 12.0 ~ 142 0

    Equat.    Gal    SGal    Ecl

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2020.07.08-04:21:16

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