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Vector Boson Fusion in the Inert Doublet Model
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abstract
In this paper we probe inert Higgs doublet model at the LHC using Vector Boson Fusion (VBF) search strategy. We optimize the selection cuts and investigate the parameter space of the model and we show that the VBF search has a better reach when compared with the monojet searches. We also investigate the Drell-Yan type cuts and show that they can be important for smaller charged Higgs masses. We determine the $3\sigma$ reach for the parameter space using these optimized cuts for a luminosity of 3000 fb$^{-1}$.
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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.
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