Annihilation vs. Decay: Constraining dark matter properties from a gamma-ray detection

TL;DR

This paper proposes a method to distinguish dark matter annihilation from decay signals in gamma-ray observations by analyzing angular distribution and spectral dependence, aiding in understanding dark matter properties.

Contribution

It introduces a novel approach to identify the nature of dark matter signals using angular and spectral analysis, enhancing the potential of gamma-ray data to constrain dark matter models.

Findings

Angular distribution analysis can differentiate decay from annihilation signals.

Spectral and intensity dependence helps identify dark matter process type.

Upcoming experiments could apply this method to broader dark matter scenarios.

Abstract

Most proposed dark matter candidates are stable and are produced thermally in the early Universe. However, there is also the possibility of unstable (but long-lived) dark matter, produced thermally or otherwise. We propose a strategy to distinguish between dark matter annihilation and/or decay in the case that a clear signal is detected in gamma-ray observations of Milky Way dwarf spheroidal galaxies with gamma-ray experiments. The sole measurement of the energy spectrum of an indirect signal would render the discrimination between these cases impossible. We show that by examining the dependence of the intensity and energy spectrum on the angular distribution of the emission, the origin could be identified as decay, annihilation, or both. In addition, once the type of signal is established, we show how these measurements could help to extract information about the dark matter properties, including mass, annihilation cross section, lifetime, dominant annihilation and decay channels, and the presence of substructure. Although an application of the approach presented here would likely be feasible with current experiments only for very optimistic dark matter scenarios, the improved sensitivity of upcoming experiments could enable this technique to be used to study a wider range of dark matter models.