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

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 a probability to each galaxy 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 continuous 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 is therefore freely available through a dedicated webpage⋆ and soon from the CasJobs database.