Within the Inert Doublet Model, machine-learning selection could make charged Higgs pair production at a 10-14 TeV muon collider a 5-sigma probe of dark matter for several benchmark points.
Searches for dark matter via charged Higgs pair production in the Inert Doublet Model at $\gamma\gamma$ collider
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abstract
The Inert Doublet Model(IDM) is one of the simplest extensions beyond Standard Model(SM) with an extended scalar sector, which provide a scalar dark matter particle candidate. In this paper, we investigate the double charged Higgs production at $\gamma\gamma$ collider. By scanning the whole parameter space, we obtain the parameter points corresponding to the correct relic abundance of dark matter. After applying all theoretical and experimental constraints, the parameter space for the existence of dark matter is extremely restricted. We perform the analysis for the signal of $H^+H^-$ production in the IDM and the SM backgrounds, and the optimized selection conditions are chosen in kinematic variables to maximize signal significance. Comparing signal with backgrounds, we obtain the parameter points which can be detected at future $\gamma\gamma$ collider experiments.
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Probing dark matter through charged Higgs pair production at future multi-TeV muon colliders: A machine-learning analysis
Within the Inert Doublet Model, machine-learning selection could make charged Higgs pair production at a 10-14 TeV muon collider a 5-sigma probe of dark matter for several benchmark points.