Identifying WIMP dark matter from particle and astroparticle data

Abstract

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. 6 Summary and conclusions 23 A Details, validation and performance of the machine learning tools 24 B Posterior distributions with a larger systematic uncertainty 25 C Profile likelihood results 26 -1 -1 Different definitions of WIMPs are used in the literature. To avoid confusion, here we use the termWIMP to refer to any particle in the O(1) GeV to O(100) TeV mass range that interacts with Standard Model particles with a strength similar to the weak-interaction, such that the abundance is obtained through the thermal freeze-out mechanism. Our definition is sometimes referred to as a 'hidden-sector WIMP' [14].

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