Export Ready — 

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

Bibliographic Details
Main Author: Castro, Nuno Filipe
Publication Date: 2023
Other Authors: Onofre, A., ATLAS Collaboration
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/1822/87833
Summary: 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.
id RCAP_76dcbcaef43227685c0e77e1210da1f3
oai_identifier_str oai:repositorium.sdum.uminho.pt:1822/87833
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision datasetCiências Naturais::Ciências FísicasThe 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.USDOE -U.S. Department of Energy(IN2P3-CNRS)info:eu-repo/semantics/publishedVersionUniversidade do MinhoCastro, Nuno FilipeOnofre, A.ATLAS Collaboration20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/87833engAad, G., Abbott, B., Abeling, K., Abicht, N. J., Abidi, S. H., Aboulhorma, A., . . . Collaboration, A. (2023). ATLAS flavour-tagging algorithms for the LHC Run 2 <i>pp</i> collision dataset. EUROPEAN PHYSICAL JOURNAL C, 83(7). doi: 10.1140/epjc/s10052-023-11699-11434-60441434-605210.1140/epjc/s10052-023-11699-1https://link.springer.com/article/10.1140/epjc/s10052-023-11699-1info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T06:07:26Zoai:repositorium.sdum.uminho.pt:1822/87833Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:41:50.880097Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
title ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
spellingShingle ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
Castro, Nuno Filipe
Ciências Naturais::Ciências Físicas
title_short ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
title_full ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
title_fullStr ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
title_full_unstemmed ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
title_sort ATLAS flavour-tagging algorithms for the LHC Run 2 pp collision dataset
author Castro, Nuno Filipe
author_facet Castro, Nuno Filipe
Onofre, A.
ATLAS Collaboration
author_role author
author2 Onofre, A.
ATLAS Collaboration
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Castro, Nuno Filipe
Onofre, A.
ATLAS Collaboration
dc.subject.por.fl_str_mv Ciências Naturais::Ciências Físicas
topic Ciências Naturais::Ciências Físicas
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.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/87833
url https://hdl.handle.net/1822/87833
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Aad, G., Abbott, B., Abeling, K., Abicht, N. J., Abidi, S. H., Aboulhorma, A., . . . Collaboration, A. (2023). ATLAS flavour-tagging algorithms for the LHC Run 2 <i>pp</i> collision dataset. EUROPEAN PHYSICAL JOURNAL C, 83(7). doi: 10.1140/epjc/s10052-023-11699-1
1434-6044
1434-6052
10.1140/epjc/s10052-023-11699-1
https://link.springer.com/article/10.1140/epjc/s10052-023-11699-1
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833595479794909184

Similar Items