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.
Multi-component Dark Matter in a Simplified E$_6$SSM Model
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abstract
We study Dark Matter (DM) in the Exceptional Supersymmetric Standard Model (E$_6$SSM). The model has both active and inert Higgs superfields and by imposing discrete symmetries one can generate two DM candidates. We show that the lightest higgsinos of the active and inert sectors give a viable setup for two-component DM. We also illustrate the scope of both direct and indirect detection experiments in extracting such a DM sector. Future experiments of the former kind have a good chance of finding the active component while the inert higgsino will be very hard to detect while those of the latter kind will have no sensitivity to either candidate.
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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.