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.
Two Component Doublet-Triplet Scalar Dark Matter stabilising the Electroweak vacuum
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abstract
A two-component scalar DM scenario comprising an additional scalar doublet and a $Y$ = 0 scalar triplet is proposed. Key features of the ensuing dark matter phenomenology are highlighted with emphasis on inter-conversion between the two dark matter components. For suitable choices of the model parameters, we show that such inter-conversion can explain the observed relic abundance when the doublet dark matter component has mass in the \emph{desert} region while the triplet component has sub-TeV mass. This finding is important in the context of such mass regions known to predict under-abundant relic for the standalone cases of the scalar doublet and triplet. In addition, we also show that the present scenario can stabilise the electroweak vacuum up to the Planck scale in the parameter space responsible for the requisite dark matter observables.
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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.