Statistical Analyses of Higgs- and Z-Portal Dark Matter Models

TL;DR

This paper conducts comprehensive statistical analyses of Higgs- and Z-portal dark matter models, integrating current experimental data and exploring future detection prospects for various dark matter particle types.

Contribution

It provides the first combined frequentist and Bayesian analysis of Higgs- and Z-portal dark matter models across multiple particle spins and experimental constraints.

Findings

Acceptable parameter regions identified for scalar, vector, and fermion dark matter.

Potential for dark matter discovery in Higgs or Z decays at colliders.

Future direct detection experiments may not exclude all viable models.

Abstract

We perform frequentist and Bayesian statistical analyses of Higgs- and Z-portal models of dark matter particles with spin 0, 1/2 and 1. Our analyses incorporate data from direct detection and indirect detection experiments, as well as LHC searches for monojet and monophoton events, and we also analyze the potential impacts of future direct detection experiments. We find acceptable regions of the parameter spaces for Higgs-portal models with real scalar, neutral vector, Majorana or Dirac fermion dark matter particles, and Z-portal models with Majorana or Dirac fermion dark matter particles. In many of these cases, there are interesting prospects for discovering dark matter particles in Higgs or Z decays, as well as dark matter particles weighing $\gtrsim 100$ GeV. Negative results from planned direct detection experiments would still allow acceptable regions for Higgs- and Z-portal models with Majorana or Dirac fermion dark matter particles.