Conservative Constraints on Dark Matter Annihilation into Gamma Rays

TL;DR

This paper derives conservative upper limits on dark matter annihilation into gamma rays using gamma-ray data from the Milky Way, Andromeda, and cosmic background, covering a wide mass range and considering various astrophysical assumptions.

Contribution

It provides the first comprehensive, conservative upper limits on dark matter annihilation cross sections into gamma rays across a broad mass spectrum, incorporating multiple observational data sources.

Findings

Gamma-ray and neutrino limits are comparable for intermediate masses.

Gamma-ray limits are stronger for low dark matter masses.

Neutrino limits dominate at high masses.

Abstract

Using gamma-ray data from observations of the Milky Way, Andromeda (M31), and the cosmic background, we calculate conservative upper limits on the dark matter self-annihilation cross section to monoenergetic gamma rays, <sigma_A v>_{gamma gamma}, over a wide range of dark matter masses. (In fact, over most of this range, our results are unchanged if one considers just the branching ratio to gamma rays with energies within a factor of a few of the endpoint at the dark matter mass.) If the final-state branching ratio to gamma rays, Br(gamma gamma), were known, then <sigma_A v>_{gamma gamma} / Br(gamma gamma) would define an upper limit on the total cross section; we conservatively assume Br(gamma gamma) > 10^{-4}. An upper limit on the total cross section can also be derived by considering the appearance rates of any Standard Model particles; in practice, this limit is defined by neutrinos, which are the least detectable. For intermediate dark matter masses, gamma-ray-based and neutrino-based upper limits on the total cross section are comparable, while the gamma-ray limit is stronger for small masses and the neutrino limit is stronger for large masses. We comment on how these results depend on the assumptions about astrophysical inputs and annihilation final states, and how GLAST and other gamma-ray experiments can improve upon them.