Using machine learning to disentangle LHC signatures of Dark Matter candidates
Using machine learning to disentangle LHC signatures of Dark Matter candidates
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 …