Using Machine Learning to disentangle LHC signatures of Dark Matter candidates

Understand

We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques.

• We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle.
• All these benchmarks are tensioned against each other, and against the main SM background ($Z$+jets).
• Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet.