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

The CMS collaboration

doi:10.1088/1748-0221/20/04/P04021

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

The CMS collaboration

A. Alikhanian Yerevan Institute of Physics
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1 Citation (Scopus)

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.

Original language	English
Article number	P04021
Journal	Journal of Instrumentation
Volume	20
Issue number	4
DOIs	https://doi.org/10.1088/1748-0221/20/04/P04021
Publication status	Published - 1 Apr 2025
Externally published	Yes

Keywords

Data processing methods
Performance of High Energy Physics Detectors

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10.1088/1748-0221/20/04/P04021

Cite this

@article{306b8d2039404a2cb11caefa05f1477d,

title = "Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV",

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.",

keywords = "Data processing methods, Performance of High Energy Physics Detectors",

author = "\{The CMS collaboration\} and V. Chekhovsky and A. Hayrapetyan and V. Makarenko and A. Tumasyan and W. Adam and Andrejkovic, \{J. W.\} and L. Benato and T. Bergauer and S. Chatterjee and K. Damanakis and M. Dragicevic and Hussain, \{P. S.\} and M. Jeitler and N. Krammer and A. Li and D. Liko and I. Mikulec and J. Schieck and R. Sch{\"o}fbeck and D. Schwarz and M. Sonawane and W. Waltenberger and Wulz, \{C. E.\} and T. Janssen and H. Kwon and \{Van Laer\}, T. and \{Van Mechelen\}, P. and N. Breugelmans and J. D'Hondt and S. Dansana and \{De Moor\}, A. and M. Delcourt and F. Heyen and Y. Hong and S. Lowette and I. Makarenko and D. M{\"u}ller and S. Tavernier and M. Tytgat and \{Van Onsem\}, \{G. P.\} and \{Van Putte\}, S. and D. Vannerom and B. Bilin and B. Clerbaux and Das, \{A. K.\} and \{De Bruyn\}, I. and \{De Lentdecker\}, G. and H. Evard and L. Favart and O. Bouhali",

note = "Publisher Copyright: {\textcopyright} 2025 CERN for the benefit of the CMS collaboration. Published by IOP Publishing Ltd on behalf of Sissa Medialab.",

year = "2025",

month = apr,

day = "1",

doi = "10.1088/1748-0221/20/04/P04021",

language = "English",

volume = "20",

journal = "Journal of Instrumentation",

issn = "1748-0221",

publisher = "Institute of Physics",

number = "4",

}

TY - JOUR

T1 - Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV

AU - The CMS collaboration

AU - Chekhovsky, V.

AU - Hayrapetyan, A.

AU - Makarenko, V.

AU - Tumasyan, A.

AU - Adam, W.

AU - Andrejkovic, J. W.

AU - Benato, L.

AU - Bergauer, T.

AU - Chatterjee, S.

AU - Damanakis, K.

AU - Dragicevic, M.

AU - Hussain, P. S.

AU - Jeitler, M.

AU - Krammer, N.

AU - Li, A.

AU - Liko, D.

AU - Mikulec, I.

AU - Schieck, J.

AU - Schöfbeck, R.

AU - Schwarz, D.

AU - Sonawane, M.

AU - Waltenberger, W.

AU - Wulz, C. E.

AU - Janssen, T.

AU - Kwon, H.

AU - Van Laer, T.

AU - Van Mechelen, P.

AU - Breugelmans, N.

AU - D'Hondt, J.

AU - Dansana, S.

AU - De Moor, A.

AU - Delcourt, M.

AU - Heyen, F.

AU - Hong, Y.

AU - Lowette, S.

AU - Makarenko, I.

AU - Müller, D.

AU - Tavernier, S.

AU - Tytgat, M.

AU - Van Onsem, G. P.

AU - Van Putte, S.

AU - Vannerom, D.

AU - Bilin, B.

AU - Clerbaux, B.

AU - Das, A. K.

AU - De Bruyn, I.

AU - De Lentdecker, G.

AU - Evard, H.

AU - Favart, L.

AU - Bouhali, O.

PY - 2025/4/1

Y1 - 2025/4/1

N2 - "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.

AB - "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.

KW - Data processing methods

KW - Performance of High Energy Physics Detectors

UR - https://www.scopus.com/pages/publications/105004415262

U2 - 10.1088/1748-0221/20/04/P04021

DO - 10.1088/1748-0221/20/04/P04021

M3 - Article

AN - SCOPUS:105004415262

SN - 1748-0221

VL - 20

JO - Journal of Instrumentation

JF - Journal of Instrumentation

IS - 4

M1 - P04021

ER -

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

Abstract

Keywords

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