"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

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at sqrt(s) = 13.6 TeV

Alexe, C.;Bruschini, D.;
2025

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
2025
Settore PHYS-01/A - Fisica sperimentale delle interazioni fondamentali e applicazioni
Data processing methods; Performance of High Energy Physics Detectors
   Advanced Multi-Variate Analysis for New Physics Searches at the LHC
   AMVA4NewPhysics
   European Commission
   Horizon 2020 Framework Programme
   675440

   Majorana neutrino discovery strategy with CMS
   MajorNet
   European Commission
   Horizon 2020 Framework Programme
   758316

   Direct and indirect searches for new physics in events with top quarks using LHC proton-proton collisions at the CMS detector
   LHCTOPVLQ
   European Commission
   Horizon 2020 Framework Programme
   752730
  
     https://doi.org/10.1088/1748-0221/20/04/P04021
     https://dx.doi.org/10.1088/1748-0221/20/04/P04021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/154987
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