Topics on Dark Matter and Active Galactic Nuclei

Abstract

Despite gravitational evidence at many spatial scales and significant experimental efforts, non-gravitational detection of dark matter has thus far been unsuccessful.Indirect dark matter detection, one of several strategies to find confirmation of the existence of dark matter and untangle its properties, aims to identify dark matter through its self-annihilation products. In particular, self-annihilation processes of thermal relic dark matter candidates (WIMPs) may produce gamma rays, providing a potentially observable signature of dark matter. After 'freezing out' of thermal equilibrium, hierarchical structure formation in cold dark matter models produces abundant `halos' and 'subhalos' of gravitationally bound dark matter clumps that host the galaxies and clusters we see today. Depending on their size and history, these halos are characterized by different dark matter velocity scales, suppressing or enhancing annihilation rates depending on the velocity-dependence of the cross section. This dissertation considers the prospects of detecting dark matter with a velocity-dependent self-annihilation cross section in dwarf spheroidal galaxies, extragalactic halos, the Milky Way center, and in galactic substructure. We will find that the angular distribution of gamma rays from dark matter annihilation and the overall normalization of the flux are sensitive to both the microphysics and the astrophysical distribution of the dark matter. A key challenge in all of these analyses are the considerations of gamma ray backgrounds from other astrophysical sources. Blazars, star-forming galaxies, and cosmic rays all contribute to gamma ray fore-/backgrounds. Without better understanding and modelling of these sources, indirect searches for dark matter are going to be stymied. The luminosity function (LF) of active galactic nuclei (AGN) describes the population of AGN as a function of redshift and luminosity. Estimates of gamma ray backgrounds from unresolved AGN can be obtained from the faint end of the AGN LF. Towards this goal, this dissertation presents the AGN LF constructed using midinfrared and X-ray data in the XMM-LSS field. I close with discussions on how AGN LFs can be used to model gamma ray backgrounds in indirect detection analyses and how newer machine learning methods may overcome some of the challenges of both current AGN LF analyses and dark matter searches.