Probing mild-tempered neutralino dark matter through top-squark production at the LHC

TL;DR

This paper investigates the potential to detect mild-tempered neutralino dark matter at the LHC through top-squark pair production, focusing on scenarios consistent with existing constraints, including blind spots, using advanced analysis techniques.

Contribution

It explores the LHC detectability of mild-tempered neutralino dark matter within MSSM, emphasizing blind spots and employing multivariate analysis for improved sensitivity.

Findings

High sensitivity to the scenario at 13 TeV with 3000 fb^{-1} luminosity.

Blind spot regions are also accessible via the proposed channel.

Multivariate analysis enhances detection prospects over cut-based methods.

Abstract

The lightest neutralino, assumed to be the lightest supersymmetric particle, is proposed to be a dark matter (DM) candidate for the mass $\cal{O}$(100) GeV. Constraints from various direct dark matter detection experiments and Planck measurements exclude a substantial region of parameter space of the minimal supersymmetric standard model (MSSM). However, a "mild-tempered" neutralino with dominant bino composition and a little admixture of Higgsino is found to be a viable candidate for DM. Within the MSSM framework, we revisit the allowed region of parameter space that is consistent with all existing constraints. Regions of parameters that are not sensitive to direct detection experiments, known as "blind spots," are also revisited. Complimentary to the direct detection of DM particles, a mild-tempered neutralino scenario is explored at the LHC with the center of mass energy $\rm \sqrt{s}$=13 TeV through the top-squark pair production, and its subsequent decays with the standard-model-like Higgs boson in the final state. Our considered channel is found to be very sensitive also to the blind spot scenario. Detectable signal sensitivities are achieved using the cut-based method for the high luminosity options $\rm 300$ and $\rm 3000 ~fb^{-1}$, which are further improved by applying the multi-variate analysis technique.