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

Sonia Kabana; Theodota Lagouri; Sebastian Andres Olivares Pino; ATLAS Collaboration

doi:10.1140/EPJC/S10052-023-11699-1

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

Translated title of the contribution: ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset

Sonia Kabana
, Theodota Lagouri
, Sebastian Andres Olivares Pino
, ATLAS Collaboration

Institute of Higher Research

Universidad de Tarapacá

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

164 Scopus citations

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.

Translated title of the contribution	ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
Original language	English
Article number	681
Pages (from-to)	1-37
Number of pages	37
Journal	European Physical Journal C
Volume	83
Issue number	7
DOIs	https://doi.org/10.1140/EPJC/S10052-023-11699-1
State	Published - 31 Jul 2023

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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 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.",

author = "Sonia Kabana and Theodota Lagouri and \{Olivares Pino\}, \{Sebastian Andres\} and \{ATLAS Collaboration\}",

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AU - Kabana, Sonia

AU - Lagouri, Theodota

AU - Olivares Pino, Sebastian Andres

AU - ATLAS Collaboration

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

Abstract

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