A 1D multi-channel CNN trained on normalized histograms of simulated mono-jet and mono-Z events can partially classify one- versus two-component dark matter and regress masses, but only in a background-free, model-specific setup.
Mono-X Versus Direct Searches: Simplified Models for Dark Matter at the LHC
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abstract
We consider simplified models for dark matter (DM) at the LHC, focused on mono-Higgs, -Z, or -b produced in the final state. Our primary purpose is to study the LHC reach of a relatively complete set of simplified models for these final states, while comparing the reach of the mono-X DM search against direct searches for the mediating particle. We find that direct searches for the mediating particle, whether in di-jets, jets+MET, multi-b+MET, or di-boson+MET, are usually stronger. We draw attention to the cases that the mono-X search is strongest, which include regions of parameter space in inelastic DM, two Higgs doublet, and squark mediated production models with a compressed spectrum.
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Mono-X Signal Characterization from Two-component Dark Matter Using a Convolutional Neural Network
A 1D multi-channel CNN trained on normalized histograms of simulated mono-jet and mono-Z events can partially classify one- versus two-component dark matter and regress masses, but only in a background-free, model-specific setup.