Identifying Dark Matter Annihilation Products In The Diffuse Gamma Ray Background

TL;DR

This paper explores methods to detect dark matter annihilation signals in the diffuse gamma-ray background by distinguishing them from unresolved blazar emissions using spectral and statistical techniques.

Contribution

It introduces novel approaches combining spectral analysis and photon distribution modeling to improve dark matter signal extraction from gamma-ray data.

Findings

Spectral differences can help separate dark matter signals from backgrounds.

Photon count distributions from blazars are non-Poissonian, affecting detection strategies.

Proper modeling reduces systematic errors in dark matter detection.

Abstract

Annihilation of cosmologically distributed dark matter is predicted to produce a potentially observable flux of high energy photons. Neglecting the contribution from local structure, this signal is predicted to be virtually uniform on the sky and, in order to be identified, must compete with various extragalactic backgrounds. We focus here on unresolved blazars and discuss several techniques for separating the dark matter signal from this background. First, the spectral shapes of the signal and background are expected to differ, a feature which can be exploited with the Fisher Matrix formalism. Second, in any given angular pixel, the number of photons from blazars is drawn from a distribution which is far from Poisson. A knowledge of this distribution enhances one's ability to extract the dark matter signal, while ignorance of it can lead to the introduction of a large systematic error.