Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

TL;DR

This paper presents a new multivariate classifier based on gradient-boosted decision trees to improve the identification of low-momentum muons in CMS data, enhancing signal-background separation for Run 3 analyses.

Contribution

A novel multivariate classifier using gradient-boosted decision trees for better low-momentum muon identification in CMS Run 3 data.

Findings

Significantly improved separation of signal and background muons.

Enhanced performance over previous classifiers used in Run 2.

Validated on 62 fb$^{-1}$ of CMS data from 2022-2023.

Abstract

"Soft" muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb$^{-1}$.