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Various galaxy classification schemes have been developed so far to constrainthe main physical processes regulating evolution of different galaxy types. Inthe era of a deluge of astrophysical information and recent progress in machinelearning, a new approach to galaxy classification becomes imperative.  We employ a Fisher Expectation-Maximization unsupervised algorithm working ina parameter space of 12 rest-frame magnitudes and spectroscopic redshift. Themodel (DBk) and the number of classes (12) were established based on the jointanalysis of standard statistical criteria and confirmed by the analysis of thegalaxy distribution with respect to a number of classes and their properties.This new approach allows us to classify galaxies based just on their redshiftsand UV-NIR spectral energy distributions.  The FEM unsupervised algorithm has automatically distinguished 12 classes: 11classes of VIPERS galaxies and an additional class of broad-line AGNs. After afirst broad division into blue, green and red categories we obtained a furthersub-division into three red, three green, and five blue galaxy classes. The FEMclasses follow the galaxy sequence from the earliest to the latest types thatis reflected in their colours (which are constructed from rest-frame magnitudesused in classification procedure) but also their morphological, physical, andspectroscopic properties (not included in the classification scheme). Wedemonstrate that the members of each class share similar physical and spectralproperties. In particular, we are able to find three different classes of redpassive galaxy populations. Thus, we demonstrate the potential of anunsupervised approach to galaxy classification and we retrieve the complexityof galaxy populations at z~0.7, a task that usual simpler colour-basedapproaches cannot fulfil.

astronomy and astrophysics

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