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