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The search for dark-matter (DM) candidates at high-energy colliders is one ofthe most promising avenues to understand the nature of this elusive componentof the universe. Several searches at the Large Hadron Collider (LHC) havestrongly constrained a wide range of simplified models. The combination of thebounds from the LHC with direct-detection experiments exclude the most minimalscalar singlet DM model. To address this, Lepton portal DM models are suitablecandidates where DM is predominantly produced at lepton colliders since the DMcandidate only interacts with the lepton sector through a mediator that carriesa lepton number. In this work, we analyse the production of DM pairs inassociation with a Higgs boson decaying into two bottom quarks at future muoncolliders in the framework of the minimal lepton portal DM model. It is foundthat the usual cut-based analysis methods fail to probe heavy DM masses forboth the resolved (where the decay products of the Higgs boson can be resolvedas two well-separated small-$R$ jets) and the merged (where the Higgs boson isclustered as one large-$R$ jet). We have then built a search strategy based onBoosted-Decision Trees (BDTs). We have optimised the hyperparameters of the BDTmodel to both have a high signal-to-background ratio and to avoid overtrainingeffects. We have found very important enhancements of the signal significancewith respect to the cut-based analysis by factors of $8$--$50$ depending on theregime (resolved or merged) and the benchmark points. Using this BDT model on aone-dimensional parameter space scan we found that future muon colliders with$\sqrt{s}=3$ TeV and ${\cal L} = 1~{\rm ab}^{-1}$ can exclude DM masses up to$1$ TeV at the $95\%$ CL.

Boosting dark matter searches at muon colliders with Machine Learning: the mono-Higgs channel as a case study