Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13 TeV

TL;DR

This paper presents two multivariate techniques developed to improve muon identification efficiency and purity in proton-proton collision data at 13 TeV collected by the CMS experiment, enhancing discrimination between prompt and nonprompt muons.

Contribution

The paper introduces novel multivariate algorithms for muon identification that outperform traditional cut-based methods in CMS data at 13 TeV.

Findings

Enhanced muon identification efficiency.

Improved discrimination between prompt and nonprompt muons.

Validated methods using 59.7 fb$^{-1}$ of data.

Abstract

The identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10 GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$^{-1}$ of proton-proton collisions data at a centre-of-mass energy of $\sqrt{s}$ = 13 TeV collected in 2018 with the CMS experiment at the CERN LHC.