Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network

TL;DR

This paper employs an artificial neural network to search for supersymmetry in LHC collision data, analyzing events with opposite-sign dileptons, jets, and missing energy, setting new limits on SUSY models.

Contribution

It introduces a novel neural network-based analysis with relaxed selection criteria to explore different SUSY parameter spaces in LHC data.

Findings

No significant excess over standard model expectations.

Set limits on SUSY particle masses within constrained models.

Probed new regions of parameter space with relaxed cuts.

Abstract

In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from a standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4.98 inverse femtobarns of proton-proton collisions at the center-of-mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (missing ET > 40 GeV) and total hadronic transverse energy (HT > 120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observations, yielding limits in the context of the constrained mininal supersymmetric standard model and on a set of simplified models.