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

ATLAS Publications; T.M. Carter; R.Y. Gonzalez Andana; A. Hasib; M.P. Heath; V.A. Parrish; E.A. Pender; E.P. Takeva; N. Themistokleous; E.M. Villhauer; E. Zaid

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

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

ATLAS Publications, T.M. Carter, R.Y. Gonzalez Andana, A. Hasib, M.P. Heath, V.A. Parrish, E.A. Pender, E.P. Takeva, N. Themistokleous, E.M. Villhauer, E. Zaid

School of Physics and Astronomy

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

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 t ¯t 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
Pages (from-to)	1-37
Number of pages	37
Journal	The European Physical Journal C
Volume	83
Issue number	7
DOIs	https://doi.org/10.1140/epjc/s10052-023-11699-1
Publication status	Published - 31 Jul 2023

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

http://arxiv.org/abs/2211.16345

3 Finished
3 Active

Upgrade of the ATLAS detector at the LHC (2023-26)
Clark, P.
1/04/23 → 31/03/26
Project: Research
Experimental Particle Physics at the University of Edinburgh
Leonidopoulos, C.
1/04/23 → 30/09/25
Project: Research
Experimental Particle Physics at the University of Edinburgh
Leonidopoulos, C.
1/10/22 → 30/09/25
Project: Research

Cite this

@article{813f9f1e50d34991948142d908df2dc1,

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

author = "{ATLAS Publications} and Clark, {Philip James} and Sinead Farrington and Yanyan Gao and Christos Leonidopoulos and Martin, {Victoria Jane} and Liza Mijovic and Benjamin Wynne and Sara Alderweireldt and T.M. Carter and {Gonzalez Andana}, R.Y. and A. Hasib and M.P. Heath and V.A. Parrish and E.A. Pender and E.P. Takeva and N. Themistokleous and E.M. Villhauer and E. Zaid",

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doi = "10.1140/epjc/s10052-023-11699-1",

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T1 - ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

AU - ATLAS Publications

AU - Clark, Philip James

AU - Farrington, Sinead

AU - Gao, Yanyan

AU - Leonidopoulos, Christos

AU - Martin, Victoria Jane

AU - Mijovic, Liza

AU - Wynne, Benjamin

AU - Alderweireldt, Sara

AU - Carter, T.M.

AU - Gonzalez Andana, R.Y.

AU - Hasib, A.

AU - Heath, M.P.

AU - Parrish, V.A.

AU - Pender, E.A.

AU - Takeva, E.P.

AU - Themistokleous, N.

AU - Villhauer, E.M.

AU - Zaid, E.

PY - 2023/7/31

Y1 - 2023/7/31

N2 - 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 t ¯t events; similarly, at a c-jet identification efficiency of 30%, a light-jet (b-jet) rejection factor of 70 (9) is obtained.

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

U2 - 10.1140/epjc/s10052-023-11699-1

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

M3 - Article

SN - 1434-6052

VL - 83

SP - 1

EP - 37

JO - The European Physical Journal C

JF - The European Physical Journal C

IS - 7

M1 - 681

ER -

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

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Projects

Upgrade of the ATLAS detector at the LHC (2023-26)

Experimental Particle Physics at the University of Edinburgh

Experimental Particle Physics at the University of Edinburgh

Cite this