Open AccessObjective classification of galaxy spectra using the information bottleneck method
Author(s) -
Slonim Noam,
Somerville Rachel,
Tishby Naftali,
Lahav Ofer
Publication year - 2001
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1046/j.1365-8711.2001.04125.x
Subject(s) - physics , galaxy , principal component analysis , velocity dispersion , spectral line , redshift , information bottleneck method , galaxy formation and evolution , astrophysics , bottleneck , artificial intelligence , astronomy , cluster analysis , computer science , embedded system
A new method for classification of galaxy spectra is presented, based on a recently introduced information theoretical principle, the information bottleneck . For any desired number of classes, galaxies are classified such that the information content about the spectra is maximally preserved. The result is classes of galaxies with similar spectra, where the similarity is determined via a measure of information. We apply our method to ∼6000 galaxy spectra from the ongoing 2dF redshift survey, and a mock‐2dF catalogue produced by a cold dark matter (CDM) based semi‐analytic model of galaxy 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, morphology, and internal velocity dispersion. We also show the correlation of the classes with the projections resulting from a principal component analysis.