Scientific article
Open access

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

ContributorsATLAS Collaboration
Published inEuropean physical journal. C, Particles and fields, vol. 83, no. 7, 681
Publication date2023
First online date2023-07-31

The flavour-tagging algorithms developed by the ATLAS Collaboration and used to analyse its dataset of $\sqrt{s} = 13$ 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 $t\bar{t}$ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.

  • P p: scattering
  • Efficiency
  • Performance
  • Neural network
  • Data analysis method
  • Numerical calculations
  • Bottom particle: particle identification
  • Charmed particle: particle identification
Citation (ISO format)
ATLAS Collaboration. ATLAS flavour-tagging algorithms for the LHC Run 2 <i>pp</i> collision dataset. In: European physical journal. C, Particles and fields, 2023, vol. 83, n° 7, p. 681. doi: 10.1140/epjc/s10052-023-11699-1
Main files (1)
Article (Published version)
ISSN of the journal1434-6044

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