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Accuracy versus precision in boosted top tagging with the ATLAS detector

Aad, G. ; Åkesson, T.P.A. LU orcid ; Astrand, K.S.V. LU ; Doglioni, C. LU ; Ekman, P.A. LU ; Hedberg, V. LU ; Herde, H. LU orcid ; Konya, B. LU ; Lytken, E. LU orcid and Poettgen, R. LU orcid , et al. (2024) In Journal of Instrumentation 19(8).
Abstract
The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √s = 13 TeV. The systematic uncertainties associated with these... (More)
The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available. © 2024 CERN for the benefit of the ATLAS collaboration. Published by IOP Publishing Ltd on behalf of Sissa Medialab. (Less)
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type
Contribution to journal
publication status
published
subject
keywords
Analysis and statistical methods, Performance of High Energy Physics Detectors, Colliding beam accelerators, Elementary particle sources, Linear accelerators, Photons, Statistical mechanics, Analyze and statistical method, ATLAS detectors, Beam axis, High energy physics detector, Measurements of, Performance, Performance of high energy physic detector, Systematic uncertainties, Top quarks, Uncertainty, Hadrons
in
Journal of Instrumentation
volume
19
issue
8
article number
P08018
publisher
IOP Publishing
external identifiers
  • scopus:85203388592
ISSN
1748-0221
DOI
10.1088/1748-0221/19/08/P08018
language
English
LU publication?
yes
id
c7d92cce-22c4-4cad-ab08-06d33507e225
date added to LUP
2025-01-10 14:24:31
date last changed
2025-04-04 15:16:22
@article{c7d92cce-22c4-4cad-ab08-06d33507e225,
  abstract     = {{The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available. © 2024 CERN for the benefit of the ATLAS collaboration. Published by IOP Publishing Ltd on behalf of Sissa Medialab.}},
  author       = {{Aad, G. and Åkesson, T.P.A. and Astrand, K.S.V. and Doglioni, C. and Ekman, P.A. and Hedberg, V. and Herde, H. and Konya, B. and Lytken, E. and Poettgen, R. and Simpson, N.D. and Smirnova, O. and Wallin, E.J. and Zwalinski, L.}},
  issn         = {{1748-0221}},
  keywords     = {{Analysis and statistical methods; Performance of High Energy Physics Detectors; Colliding beam accelerators; Elementary particle sources; Linear accelerators; Photons; Statistical mechanics; Analyze and statistical method; ATLAS detectors; Beam axis; High energy physics detector; Measurements of; Performance; Performance of high energy physic detector; Systematic uncertainties; Top quarks; Uncertainty; Hadrons}},
  language     = {{eng}},
  number       = {{8}},
  publisher    = {{IOP Publishing}},
  series       = {{Journal of Instrumentation}},
  title        = {{Accuracy versus precision in boosted top tagging with the ATLAS detector}},
  url          = {{http://dx.doi.org/10.1088/1748-0221/19/08/P08018}},
  doi          = {{10.1088/1748-0221/19/08/P08018}},
  volume       = {{19}},
  year         = {{2024}},
}