Preparing for Dark Matter: Maximising our discrimination power in the event of detection

Abstract

Numerous experimental observations place Dark Matter (DM) as a central character in our cosmological history. Many extensions to the Standard Model of particles physics provide candidates for DM, often predicting interactions additional to gravity. This gives us the opportunity to experimentally probe these extensions and determine the nature of DM. In this thesis, we explore how direct DM detection could be used most effectively to achieve this goal. With this in mind, we have developed a tool for performing multidimensional parameter scans. This tool allows us to evaluate the capabilities of current and future detectors for detecting and understanding DM interactions. We show that by extending the energy region analysed, detection sensitivities and parameter reconstruction can be improved substantially. These insights play an important role in more global analyses, where hints of DM could come from other experiments, but verification depends on direct detection.