Beyond the best-fit parameter: new insight on galaxy structure decomposition from GALPHAT

TL;DR

GALPHAT is a new Bayesian image decomposition tool that accurately estimates galaxy surface brightness profiles and provides statistical confidence, aiding in galaxy formation studies.

Contribution

Introduces GALPHAT, a Bayesian MCMC-based package for galaxy image decomposition, with robust parameter estimation and confidence intervals.

Findings

GALPHAT provides reliable galaxy profile parameters.

It is fast enough for large galaxy surveys.

It enables testing complex galaxy evolution hypotheses.

Abstract

We introduce a novel image decomposition package, GALPHAT, that provides robust estimates of galaxy surface brightness profiles using Bayesian Markov Chain Monte Carlo. The GALPHAT-determined posterior distribution of parameters enables us to assign rigorous statistical confidence intervals to maximum a posteriori estimates and to test complex galaxy formation and evolution hypotheses. We describe the GALPHAT algorithm, assess its performance using test image data, and demonstrate that it has sufficient speed for production analysis of a large galaxy sample. Finally we briefly introduce our ongoing science program to study the distribution of galaxy structural properties in the local universe using GALPHAT.