Approximate Bayesian Computation Applied to the Diffuse Gamma-ray Sky

TL;DR

This paper demonstrates that Approximate Bayesian Computation (ABC) effectively analyzes the diffuse gamma-ray background, providing tighter constraints on source contributions, including dark matter, especially when energy information is incorporated.

Contribution

The paper introduces the application of ABC to diffuse gamma-ray background analysis, enabling efficient parameter estimation with energy data that is otherwise computationally intractable.

Findings

ABC yields results consistent with exact likelihood when energy data is ignored.

Including energy information with ABC tightens parameter constraints.

ABC is a powerful tool for source contribution analysis in gamma-ray astrophysics.

Abstract

Many sources contribute to the diffuse gamma-ray background (DGRB), including star forming galaxies, active galactic nuclei, and cosmic ray interactions in the Milky Way. Exotic sources, such as dark matter annihilation, may also make some contribution. The photon counts-in-pixels distribution is a powerful tool for analyzing the DGRB and determining the relative contributions of different sources. However, including photon energy information in a likelihood analysis of the counts-in-pixels distribution quickly becomes computationally intractable as the number of source types and energy bins increase. Here, we apply the likelihood-free method of Approximate Bayesian Computation (ABC) to the problem. We consider a mock analysis that includes contributions from dark matter annihilation in galactic subhalos as well as astrophysical backgrounds. We show that our results using ABC are consistent with the exact likelihood when energy information is discarded, and that significantly tighter parameter constraints can be obtained with ABC when energy information is included. ABC presents a powerful tool for analyzing the DGRB and understanding its varied origins.