Searches for Dark Matter at the LHC: A Multivariate Analysis in the Mono-$Z$ Channel

TL;DR

This study explores the potential of the LHC to detect dark matter via the mono-Z channel using advanced multivariate analysis, achieving significantly improved sensitivity over traditional methods.

Contribution

It introduces a multivariate likelihood-based analysis for dark matter searches in the mono-Z channel, enhancing the reach and robustness compared to simple cut-and-count techniques.

Findings

Multivariate analysis doubles the sensitivity reach compared to simple methods.

The 5σ reach for DM interaction scale is up to 3 TeV with 3 ab$^{-1}$.

The analysis shows increased stability against systematic uncertainties.

Abstract

We study dark matter (DM) production in the mono-Z channel at the 13 TeV LHC both in an effective field theory framework as well as in simplified models with vector mediators, using a multivariate analysis. For DM-quark effective operators with scalar, vector, and tensor couplings and DM mass of 100 GeV, the 5$\sigma$ reach in the DM interaction scale $\Lambda$ is around 2, 1, and 3 TeV, respectively, for 3 ab$^{-1}$ and assuming a 5\% systematic uncertainty on the total background normalization. For simplified models with leptophobic vector mediators, the 5$\sigma$ reach for the mass of the mediator is 1.7 TeV also assuming a 5\% systematics and 3 ab$^{-1}$ of integrated luminosity. The reach for the dark matter interaction scale obtained with the multivariate analysis using a likelihood function discriminant is at least twice as high as that obtained from a simple cut and count analysis, once systematics on the background normalization larger than a few percent are taken into account. Moreover, the reach is much more stable against degradation due these systematic uncertainties.