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

ATLAS Collaboration

Preprint

PUBDB-2022-07539

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

ATLAS Collaboration (Collaboration author)DESY*

2022

[10.3204/PUBDB-2022-07539]

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Please use a persistent id in citations: doi:10.3204/PUBDB-2022-07539

Report No.: CERN-EP-2022-226; arXiv:2211.16345

Abstract: 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 70% $b$-jet identification efficiency operating point, light-jet (charm-jet) rejection factors of 600 (11) 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.

Keyword(s): p p: scattering ; efficiency ; ATLAS ; performance ; neural network ; data analysis method ; numerical calculations ; bottom particle: particle identification ; charmed particle: particle identification

Note: 34 pages in total, 19 figures, 2 tables, submitted to EPJC. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/FTAG-2019-07/

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