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

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 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.

Item Metadata

Title
ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
Creator
ATLAS Collaboration
Publisher
Springer Berlin Heidelberg
Date Issued
2023-07-31
Description
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.
Genre
Article
Type
Text
Language
eng
Date Available
2023-09-18
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0435920
URI
http://hdl.handle.net/2429/85961
Affiliation
Science, Faculty of; TRIUMF; Non UBC; Physics and Astronomy, Department of
Citation
The European Physical Journal C. 2023 Jul 31;83(7):681
Publisher DOI
10.1140/epjc/s10052-023-11699-1
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher
Copyright Holder
The Author(s)
Rights URI
http://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

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Rights

Attribution 4.0 International (CC BY 4.0)