Morphology of galaxies in the WINGS clusters

TL;DR

This paper introduces MORPHOT, an automated tool for galaxy morphological classification in WINGS clusters, providing a large catalog with both automatic and visual classifications, and analyzing galaxy properties and distributions.

Contribution

The paper presents MORPHOT, a novel non-parametric, empirical tool combining ML and NN techniques for galaxy morphology classification, calibrated with visual classifications in the WINGS survey.

Findings

MORPHOT achieves high accuracy in galaxy classification.

The catalog includes morphological types, ellipticities, colors, and Sersic indices.

Galaxy morphological fractions vary with clustercentric distance.

Abstract

We present the morphological catalog of galaxies in nearby clusters of the WINGS survey (Fasano et al. 2006). The catalog contains a total number of 39923 galaxies, for which we provide the automatic estimates of the morphological type applying the purposely devised tool MORPHOT to the V-band WINGS imaging. For ~3000 galaxies we also provide visual estimates of the morphological types. A substantial part of the paper is devoted to the description of the MORPHOT tool, whose application is limited, at least for the moment, to the WINGS imaging only. The approach of the tool to the automation of morphological classification is a non parametric and fully empiri- cal one. In particular, MORPHOT exploits 21 morphological diagnostics, directly and easily computable from the galaxy image, to provide two independent classifications: one based on a Maximum Likelihood (ML), semi-analytical technique, the other one on a Neural Network (NN) machine. A suitably selected sample of ~1000 visually clas- sified WINGS galaxies is used to calibrate the diagnostics for the ML estimator and as a training set in the NN machine. The final morphological estimator combines the two techniques and proves to be effective both when applied to an additional test sample of ~1000 visually classified WINGS galaxies and when compared with small samples of SDSS galaxies visually classified by Fukugita et al. (2007) and Nair et al. (2010). Finally, besides the galaxy morphology distribution (corrected for field contamination) in the WINGS clusters, we present the ellipticity ({\epsilon}), color (B-V) and Sersic index (n) distributions for different morphological types, as well as the morphological fractions as a function of the clustercentric distance (in units of R200).