Search for R-parity violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using $\sqrt{s} = 13$ TeV proton-proton collision data

TL;DR

This paper reports a search for R-parity violating supersymmetry in high jet multiplicity final states with leptons and b-jets using ATLAS data, employing data-driven background estimation and machine learning, but finds no evidence of new physics.

Contribution

It introduces a novel analysis combining data-driven background modeling with machine learning to enhance sensitivity to R-parity violating supersymmetry signals.

Findings

No significant excess over Standard Model expectations.

Exclusion limits up to 2.4 TeV for gluino mass.

Extended sensitivity to electroweakino production.

Abstract

A search for R-parity violating supersymmetry in final states characterised by high jet multiplicity, at least one isolated light lepton and either zero or at least three $b$-tagged jets is presented. The search uses 139 fb$^{-1}$ of $\sqrt{s} = 13$ TeV proton-proton collision data collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature gluino production, top-squark production, or electroweakino production. The dominant sources of background are estimated using a data-driven model, based on observables at medium jet multiplicity, to predict the $b$-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. Machine learning techniques are used to reach sensitivity to electroweakino production, extending the data-driven background estimation to the shape of the machine learning discriminant. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence-level are extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark mass, and 320 (365) GeV in higgsino (wino) mass.