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Article
Report number arXiv:2012.00578 ; CERN-EP-2020-199
Title Muon reconstruction and identification efficiency in ATLAS using the full Run 2 pp collision data set at s=13 TeV
Author(s) ATLAS Collaboration  Zeige alle 3014 Autoren
Corporate Author(s) ATLAS Collaboration
Publication 2021-07-05
Imprint 02 Dec 2020
Number of pages 44
Note 64 pages in total, author list starting page 42, auxiliary material starting at page 59, 34 figures, 3 tables. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/MUON-2018-03/
In: Eur. Phys. J., C 81 (2021) 578
DOI 10.1140/epjc/s10052-021-09233-2 (publication)
Subject category Particle Physics - Experiment
Accelerator/Facility, Experiment CERN LHC ; ATLAS
Free keywords detector ; particle identification ; experimental results
Abstract This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 fb1 of pp collision data at s=13 TeV collected between 2015 and 2018 during Run 2 of the LHC. The increased instantaneous luminosity delivered by the LHC over this period required a reoptimisation of the criteria for the identification of prompt muons. Improved and newly developed algorithms were deployed to preserve high muon identification efficiency with a low misidentification rate and good momentum resolution. The availability of large samples of Zμμ and J/ψμμ decays, and the minimisation of systematic uncertainties, allows the efficiencies of criteria for muon identification, primary vertex association, and isolation to be measured with an accuracy at the per-mille level in the bulk of the phase space, and up to the percent level in complex kinematic configurations. Excellent performance is achieved over a range of transverse momenta from 3 GeV to several hundred GeV, and across the full muon detector acceptance of |η|<2.7.
Related document supersedes: ATLAS-CONF-2020-030
Copyright/License publication: © 2021-2025 CERN (License: CC-BY-4.0), sponsored by SCOAP³
preprint: © 2020-2025 CERN (License: CC-BY-4.0)



Corresponding record in: Inspire
Email contact: tatjana.lenz@cern.ch


 Datensatz erzeugt am 2020-12-02, letzte Änderung am 2024-12-13


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