Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at √s=13 TeV
File(s)
Published version
Author(s)
Type
Journal Article
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 √
𝑠 = 13 TeV collected in 2018 with the CMS experiment
at the CERN LHC.
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 √
𝑠 = 13 TeV collected in 2018 with the CMS experiment
at the CERN LHC.
Date Issued
2024-02
Date Acceptance
2023-11-02
Citation
Journal of Instrumentation, 2024, 19 (2)
ISSN
1748-0221
Publisher
IOP Publishing
Journal / Book Title
Journal of Instrumentation
Volume
19
Issue
2
Copyright Statement
©2024 CERN for the benefit of the CMS collaboration. Published by
IOP Publishing Ltd on behalf of Sissa Medialab. Original content from
this work may be used under the terms of the Creative Commons Attribution 4.0 licence.
Any further distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
IOP Publishing Ltd on behalf of Sissa Medialab. Original content from
this work may be used under the terms of the Creative Commons Attribution 4.0 licence.
Any further distribution of this work must maintain attribution to the author(s) and the
title of the work, journal citation and DOI.
License URL
Identifier
https://iopscience.iop.org/article/10.1088/1748-0221/19/02/P02031
Subjects
Instruments & Instrumentation
Muon spectrometers
Particle identification methods
Particle tracking detectors
Science & Technology
Technology
Publication Status
Published
Article Number
P02031
Date Publish Online
2024-02-23