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arXiv · 2112.00255

Approximate Bayesian Computation Applied to the Diffuse Gamma-ray Sky

Eric J. Baxter·J. G. Christy·Jason Kumar
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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.

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Eric J. Baxter, J. G. Christy, Jason Kumar. 2021-12-01. Approximate Bayesian Computation Applied to the Diffuse Gamma-ray Sky. https://doi.org/10.1093/mnras%2Fstac2409

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