Multilepton signatures for scalar dark matter searches in coannihilation scenario

TL;DR

This paper explores a scalar dark matter model with a dark lepton partner, analyzing new annihilation and production channels, and employs machine learning techniques to enhance detection prospects at the LHC.

Contribution

It introduces a novel scalar singlet dark matter scenario with a vectorlike fermionic doublet, revealing new annihilation and production channels and applying boosted decision trees for improved signal detection.

Findings

Enhanced dark matter annihilation and coannihilation channels identified.

Boosted decision trees effectively distinguish leptonic signals from background.

Potential for improved dark matter detection at the LHC.

Abstract

We revisit the scalar singlet dark matter (DM) scenario with a pair of dark lepton partners which form a vectorlike Dirac fermionic doublet. The extra doublet couples with the Standard Model (SM) leptonic doublet and the scalar singlet via a non-SM-like Yukawa structure. As a result, (i) since the extra fermionic states interact with other dark sector particles as well as the SM via gauge and Yukawa interactions, it gives rise to new DM annihilation processes including pair annihilation as well as coannihilation channels, and (ii) such a Yukawa structure opens up new production channels for leptonic final states giving much enhancement in cross sections to search for dark matter in the LHC. Using suitable kinematic observables, we train a {\em boosted decision tree} (BDT) classifier to separate enhanced but still feeble light leptonic signals from the background in an effective manner. On the other hand, the same technique is applied to study $\tau$-tagged jets in the search for DM signals.