The VIMOS Public Extragalactic Redshift Survey (VIPERS). Unsupervised
  classification with photometric redshifts: a method to accurately classify
  large galaxy samples without spectroscopic information
  Techniques to classify galaxies solely based on photometry will be necessary
for future large cosmology missions, such as Euclid or LSST. However, the
precision of classification is always lower in photometric surveys and can be
systematically biased with respect to classifications based upon spectroscopic
data. We verified how precisely the detailed classification scheme introduced
by Siudek et al. (2018, hereafter: S1) for galaxies at z~0.7 could be
reproduced if only photometric data are available. We applied the Fisher
Expectation-Maximization (FEM) unsupervised clustering algorithm to 54,293
VIPERS galaxies working in a parameter space of reliable photometric redshifts
and 12 corresponding rest-frame magnitudes. The FEM algorithm distinguishes
four main groups: (1) red, (2) green, (3) blue, and (4) outliers. Each group is
further divided into 3, 3, 4, and 2 subclasses, respectively. The accuracy of
reproducing galaxy classes using spectroscopic data is high: 92%, 84%, 96% for
red, green, and blue classes, respectively, except for dusty star-forming
galaxies. The presented verification of the photometric classification
demonstrates that large photometric samples can be used to distinguish
different galaxy classes at z > 0.5 with an accuracy provided so far only by
spectroscopic data except for particular galaxy classes.
