The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity
  of galaxy populations at 0.4< z<1.3 revealed with unsupervised
  machine-learning algorithms
  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 becomes imperative.
  We employ a Fisher Expectation-Maximization 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 just on their redshifts
and UV-NIR spectral energy distributions.
  The FEM unsupervised algorithm has automatically distinguished 12 classes: 11
classes of VIPERS galaxies and an additional class of broad-line 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 that
is reflected in their colours (which are constructed from rest-frame magnitudes
used in 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.
