Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available bayesian automated classification

TL;DR

This paper introduces a Bayesian automated galaxy classification method using support vector machines that assigns probabilities to each galaxy's morphological type, improving upon traditional single-class labels.

Contribution

It presents a novel probabilistic classification approach for galaxy morphology based on machine learning, trained on visual classifications, with a publicly available catalog.

Findings

High correlation with visual classifications

Probabilistic approach captures morphological transitions

Catalog available for diverse applications

Abstract

We present an automated morphological classification in 4 types (E,S0,Sab,Scd) of ~700.000 galaxies from the SDSS DR7 spectroscopic sample based on support vector machines. The main new property of the classification is that we associate to each galaxy a probability of being in the four morphological classes instead of assigning a single class. The classification is therefore better adapted to nature where we expect a continuos transition between different morphological types. The algorithm is trained with a visual classification and then compared to several independent visual classifications including the Galaxy Zoo first release catalog. We find a very good correlation between the automated classification and classical visual ones. The compiled catalog is intended for use in different applications and can be downloaded at http://gepicom04.obspm.fr/sdss_morphology/Morphology_2010.html and soon from the CasJobs database.