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Techniques to classify galaxies solely based on photometry will be necessaryfor future large cosmology missions, such as Euclid or LSST. However, theprecision of classification is always lower in photometric surveys and can besystematically biased with respect to classifications based upon spectroscopicdata. We verified how precisely the detailed classification scheme introducedby Siudek et al. (2018, hereafter: S1) for galaxies at z~0.7 could bereproduced if only photometric data are available. We applied the FisherExpectation-Maximization (FEM) unsupervised clustering algorithm to 54,293VIPERS galaxies working in a parameter space of reliable photometric redshiftsand 12 corresponding rest-frame magnitudes. The FEM algorithm distinguishesfour main groups: (1) red, (2) green, (3) blue, and (4) outliers. Each group isfurther divided into 3, 3, 4, and 2 subclasses, respectively. The accuracy ofreproducing galaxy classes using spectroscopic data is high: 92%, 84%, 96% forred, green, and blue classes, respectively, except for dusty star-forminggalaxies. The presented verification of the photometric classificationdemonstrates that large photometric samples can be used to distinguishdifferent galaxy classes at z > 0.5 with an accuracy provided so far only byspectroscopic data except for particular galaxy classes.

The VIMOS Public Extragalactic Redshift Survey (VIPERS). Unsupervised  classification with photometric redshifts: a method to accurately classify  large galaxy samples without spectroscopic information