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تسجيل مستخدم جديدLHC signals of triplet scalars as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks
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We consider a scenario where an SU(2) triplet scalar acts as the portal for a scalar dark matter particle. We identify regions of the parameter space, where such a triplet coexists with the usual Higgs doublet consistently with all theoretical as well as neutrino, accelerator and dark matter constraints, and the triplet-dominated neutral state has substantial invisible branching fraction. LHC signals are investigated for such regions, in the final state {em same-sign dilepton + $ge$ 2 jets + $ ot E_T$.} While straightforward detectability at the high-luminosity run is predicted for some benchmark points in a cut-based analysis, there are other benchmarks where one has to resort to gradient boosting/neural network techniques in order to achieve appreciable signal significance.
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