The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Else Lytken

Else Lytken

Professor

Else Lytken

Accuracy versus precision in boosted top tagging with the ATLAS detector

Author

  • G. Aad
  • T.P.A. Åkesson
  • K.S.V. Astrand
  • C. Doglioni
  • P.A. Ekman
  • V. Hedberg
  • H. Herde
  • B. Konya
  • E. Lytken
  • R. Poettgen
  • N.D. Simpson
  • O. Smirnova
  • E.J. Wallin
  • L. Zwalinski
Show all

Summary, in English

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.

Department/s

  • Particle and nuclear physics
  • eSSENCE: The e-Science Collaboration
  • Department of Physics

Publishing year

2024

Language

English

Publication/Series

Journal of Instrumentation

Volume

19

Issue

8

Document type

Journal article

Publisher

IOP Publishing

Topic

  • Subatomic Physics

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

Status

Published

ISBN/ISSN/Other

  • ISSN: 1748-0221