Collider Signatures of Type-X 2HDM + scalar singlet dark matter at HL-LHC

TL;DR

This paper explores collider signatures of a Type-X 2HDM with scalar singlet dark matter at HL-LHC, using neural networks to improve detection prospects and addressing dark matter and muon g-2 anomalies.

Contribution

It introduces a novel collider analysis for Type-X 2HDM with scalar singlet dark matter, employing neural networks for enhanced signal detection.

Findings

Neural network approach improves signal significance.

Identifies parameter regions accessible at HL-LHC.

Provides collider signatures specific to this dark matter model.

Abstract

As the 125 GeV Higgs becomes disfavored as a portal to the dark sector, one is motivated to look beyond the SM-Higgs sector, into extended scalar-mediated portal mechanisms. In this work we consider one interesting possibility of such extended scalar sector, namely Type X two Higgs doublet with a scalar singlet dark matter. This model with the advantage of light pseudoscalars also provides the explanation for the observed anomalous magnetic moment of muon. The dark matter phenomenology of this model is unique from the other types of two Higgs doublet models. Therefore, it is quite intriguing to look for signatures specific to this model at the collider experiments. In this work we take up the task of finding suitable final states and regions of parameters space that can be probed at the high-luminosity runs of LHC. We go beyond our rectangular cut-based approach and use Artificial Neural Network which shows remarkable improvement in terms of signal significance.