Identifying WIMP dark matter from particle and astroparticle data

TL;DR

This paper explores how combining collider and direct detection data, analyzed with machine learning, can identify WIMP dark matter and test its consistency with cosmological relic density, revealing potential new physics or non-standard cosmology.

Contribution

It demonstrates a method to identify WIMP dark matter and assess its relic density using combined experimental data and machine learning within simplified models.

Findings

Combined data can confirm WIMP nature of dark matter.

Inconsistencies suggest additional dark sector physics or non-standard cosmology.

Machine learning accelerates statistical inference in dark matter analysis.

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.