A study on the statistical significance of mutual information between morphology of a galaxy and its large-scale environment

Abstract

A non-zero mutual information between morphology of a galaxy and its large-scale environment is known to exist in SDSS upto a few tens of Mpc. It is important to test the statistical significance of these mutual information if any. We propose three different methods to test the statistical significance of these non-zero mutual information and apply them to SDSS and Millennium Run simulation. We randomize the morphological information of SDSS galaxies without affecting their spatial distribution and compare the mutual information in the original and randomized datasets. We also divide the galaxy distribution into smaller subcubes and randomly shuffle them many times keeping the morphological information of galaxies intact. We compare the mutual information in the original SDSS data and its shuffled realizations for different shuffling lengths. Using a t-test, we find that a small but statistically significant (at 99.9% confidence level) mutual information between morphology and environment exists upto the entire length scale probed. We also conduct another experiment using mock datasets from a semi-analytic galaxy catalogue where we assign morphology to galaxies in a controlled manner based on the density at their locations. The experiment clearly demonstrate that mutual information can effectively capture the physical correlations between morphology and environment. Our analysis suggests that physical association between morphology and environment may extend to much larger length scales than currently believed and the information theoretic framework presented here, can serve as a sensitive and useful probe of the assembly bias and large-scale environmental dependence of galaxy properties.

15 pages, 9 figures, 4 tables. Accepted for publication in MNRAS. The revised version includes analysis with dark matter halo catalogue from Millennium simulation. Comments and suggestions are welcome

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