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

ATLAS Collaboration; Paul Newman

doi:10.1140/epjc/s10052-023-11699-1

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

ATLAS Collaboration, Paul Newman

Physics and Astronomy

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Abstract

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 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 tt̅ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.

Original language	English
Article number	681
Number of pages	37
Journal	European Physical Journal C
Volume	83
DOIs	https://doi.org/10.1140/epjc/s10052-023-11699-1
Publication status	Published - 31 Jul 2023

Bibliographical 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/

Keywords

physics.data-an
hep-ex

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10.1140/epjc/s10052-023-11699-1Licence: Creative Commons: Attribution (CC BY)

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title = "ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset",

abstract = "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 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 tt̅ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.",

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AB - 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 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 tt̅ events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.

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DO - 10.1140/epjc/s10052-023-11699-1

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VL - 83

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JF - European Physical Journal C

M1 - 681

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

Abstract

Bibliographical note

Keywords

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