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Göran Jarlskog

Göran Jarlskog

Professor emeritus

Göran Jarlskog

ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

Author

  • G. Aad
  • T.P.A. Åkesson
  • E.E. Corrigan
  • C. Doglioni
  • P.A. Ekman
  • J. Geisen
  • V. Hedberg
  • H. Herde
  • G. Jarlskog
  • B. Konya
  • E. Lytken
  • J.U. Mjörnmark
  • R. Poettgen
  • N.D. Simpson
  • E. Skorda
  • O. Smirnova
  • L. Zwalinski

Summary, in English

The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of s=13 TeV pp collisions from Run 2 of the Large Hadron Collider are presented. These new tagging algorithms are based on recurrent and deep neural networks, and their performance is evaluated in simulated collision events. These developments yield considerable improvements over previous jet-flavour identification strategies. At the 77% b-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 170 (5) are achieved in a sample of simulated Standard Model tt¯ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained. © 2023, The Author(s).

Department/s

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

Publishing year

2023

Language

English

Publication/Series

European Physical Journal C

Volume

83

Issue

7

Document type

Journal article

Publisher

Springer

Topic

  • Subatomic Physics

Status

Published

ISBN/ISSN/Other

  • ISSN: 1434-6044