Identifying WIMP dark matter from particle and astroparticle data

Abstract

One of the most promising strategies to identify the nature of dark matter\nconsists in the search for new particles at accelerators and with so-called\ndirect detection experiments. Working within the framework of simplified\nmodels, and making use of machine learning tools to speed up statistical\ninference, we address the question of what we can learn about dark matter from\na detection at the LHC and a forthcoming direct detection experiment. We show\nthat with a combination of accelerator and direct detection data, it is\npossible to identify newly discovered particles as dark matter, by\nreconstructing their relic density assuming they are weakly interacting massive\nparticles (WIMPs) thermally produced in the early Universe, and demonstrating\nthat it is consistent with the measured dark matter abundance. An inconsistency\nbetween these two quantities would instead point either towards additional\nphysics in the dark sector, or towards a non-standard cosmology, with a thermal\nhistory substantially different from that of the standard cosmological model.\n