Objective classification of galaxy spectra using the information bottleneck method

A new method for classification of galaxy spectra is presented, based on a recently introduced information theoretical principle, the information bott leneck. For any desired number of classes, galaxies are classified such tha t the information content about the spectra is maximally preserved. The res ult is classes of galaxies with similar spectra, where the similarity is de termined via a measure of information. We apply our method to similar to 60 00 galaxy spectra from the ongoing 2dF redshift survey, and a mock-2dF cata logue produced by a cold dark matter (CDM) based semi-analytic model of gal axy formation. We find a good match between the mean spectra of the classes found in the data and in the models. For the mock catalogue, we find that the classes produced by our algorithm form an intuitively sensible sequence in terms of physical properties such as colour, star formation activity, m orphology, and internal velocity dispersion. We also show the correlation o f the classes with the projections resulting from a principal component ana lysis.