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.
The $Z_5$ model of two-component dark matter
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abstract
Scenarios for multi-component scalar dark matter based on a single $Z_N$ ($N\geq 4$) symmetry are simple and well-motivated. In this paper we investigate, for the first time, the phenomenology of the $Z_5$ model for two-component dark matter. This model, which can be seen as an extension of the well-known singlet scalar model, features two complex scalar fields--the dark matter particles--that are Standard Model singlets but have different charges under a $Z_5$ symmetry. The interactions allowed by the $Z_5$ give rise to novel processes between the dark matter particles that affect their relic densities and their detection prospects, which we study in detail. The key parameters of the model are identified and its viable regions are characterized by means of random scans. We show that, unlike the singlet scalar model, dark matter masses below the TeV are still compatible with present data. Even though the dark matter density turns out to be dominated by the lighter component, we find that current and future direct detection experiments may be sensitive to signals from both dark matter particles.
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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.