Approximate Bayesian Computation Applied to the Diffuse Gamma-Ray Sky

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. Although informative, 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. I will present how the likelihood-free method of Approximate Bayesian Computation (ABC) can be applied to the problem. I consider a mock analysis that includes contributions from dark matter annihilation in galactic subhalos as well as astrophysical backgrounds. I will show that the results acquired 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. Likelihood free methods such as ABC offer potent tools for analyzing the DGRB and understanding its varied origins.