A maximum likelihood method for bidimensional experimental distributions, and its application to the galaxy merger fraction

TL;DR

This paper introduces a maximum likelihood method to correct observational biases in measuring galaxy merger fractions from bidimensional distributions, improving accuracy in the presence of errors.

Contribution

The paper develops and tests a maximum likelihood approach to accurately recover galaxy distributions in redshift-asymmetry space, accounting for observational errors.

Findings

Method effectively recovers true galaxy distributions despite observational errors.

Accuracy depends on catalog size and error magnitude.

Applicable to any bidimensional distribution with observational uncertainties.

Abstract

The determination of galaxy merger fraction of field galaxies using automatic morphological indices and photometric redshifts is affected by several biases if observational errors are not properly treated. Here, we correct these biases using maximum likelihood techniques. The method takes into account the observational errors to statistically recover the real shape of the bidimensional distribution of galaxies in redshift - asymmetry space, needed to infer the redshift evolution of galaxy merger fraction. We test the method with synthetic catalogs and show its applicability limits. The accuracy of the method depends on catalog characteristics such as the number of sources or the experimental error sizes. We show that the maximum likelihood method recovers the real distribution of galaxies in redshift and asymmetry space even when binning is such that bin sizes approach the size of the observational errors. We provide a step-by-step guide to applying maximum likelihood techniques to recover any one- or bidimensional distribution subject to observational errors.