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 similar to 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(star) and soon from the CasJobs database.