Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC

Autores
Alconada Verzini, María Josefina; Alonso, Francisco; Arduh, Francisco Anuar; Dova, María Teresa; Hoya, Joaquín; Monticelli, Fernando Gabriel; Orellana, Gonzalo Enrique; Wahlberg, Hernán Pablo; The ATLAS Collaboration
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at s = 13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1 fb - 1 for the tt¯ and γ+ jet and 36.7 fb - 1 for the dijet event topologies. © 2019, CERN for the benefit of the ATLAS collaboration.
La lista completa de autores puede verse en el archivo asociado.
Instituto de Física La Plata
Materia
Física
Particle physics
Physics
Jet (particle physics)
Top quark
Atlas (anatomy)
Atlas experiment
Particle identification
Quark
Boson
Large hadron collider
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/124624
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHCAlconada Verzini, María JosefinaAlonso, FranciscoArduh, Francisco AnuarDova, María TeresaHoya, JoaquínMonticelli, Fernando GabrielOrellana, Gonzalo EnriqueWahlberg, Hernán PabloThe ATLAS CollaborationFísicaParticle physicsPhysicsJet (particle physics)Top quarkAtlas (anatomy)Atlas experimentParticle identificationQuarkBosonLarge hadron colliderThe performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at s = 13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1 fb - 1 for the tt¯ and γ+ jet and 36.7 fb - 1 for the dijet event topologies. © 2019, CERN for the benefit of the ATLAS collaboration.La lista completa de autores puede verse en el archivo asociado.Instituto de Física La Plata2019-04-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/124624enginfo:eu-repo/semantics/altIdentifier/issn/1434-6044info:eu-repo/semantics/altIdentifier/issn/1434-6052info:eu-repo/semantics/altIdentifier/arxiv/1808.07858info:eu-repo/semantics/altIdentifier/doi/10.1140/epjc/s10052-019-6847-8info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:29:53Zoai:sedici.unlp.edu.ar:10915/124624Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:29:53.675SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
title Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
spellingShingle Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
Alconada Verzini, María Josefina
Física
Particle physics
Physics
Jet (particle physics)
Top quark
Atlas (anatomy)
Atlas experiment
Particle identification
Quark
Boson
Large hadron collider
title_short Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
title_full Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
title_fullStr Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
title_full_unstemmed Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
title_sort Performance of top-quark and W -boson tagging with ATLAS in Run 2 of the LHC
dc.creator.none.fl_str_mv Alconada Verzini, María Josefina
Alonso, Francisco
Arduh, Francisco Anuar
Dova, María Teresa
Hoya, Joaquín
Monticelli, Fernando Gabriel
Orellana, Gonzalo Enrique
Wahlberg, Hernán Pablo
The ATLAS Collaboration
author Alconada Verzini, María Josefina
author_facet Alconada Verzini, María Josefina
Alonso, Francisco
Arduh, Francisco Anuar
Dova, María Teresa
Hoya, Joaquín
Monticelli, Fernando Gabriel
Orellana, Gonzalo Enrique
Wahlberg, Hernán Pablo
The ATLAS Collaboration
author_role author
author2 Alonso, Francisco
Arduh, Francisco Anuar
Dova, María Teresa
Hoya, Joaquín
Monticelli, Fernando Gabriel
Orellana, Gonzalo Enrique
Wahlberg, Hernán Pablo
The ATLAS Collaboration
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Física
Particle physics
Physics
Jet (particle physics)
Top quark
Atlas (anatomy)
Atlas experiment
Particle identification
Quark
Boson
Large hadron collider
topic Física
Particle physics
Physics
Jet (particle physics)
Top quark
Atlas (anatomy)
Atlas experiment
Particle identification
Quark
Boson
Large hadron collider
dc.description.none.fl_txt_mv The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at s = 13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1 fb - 1 for the tt¯ and γ+ jet and 36.7 fb - 1 for the dijet event topologies. © 2019, CERN for the benefit of the ATLAS collaboration.
La lista completa de autores puede verse en el archivo asociado.
Instituto de Física La Plata
description The performance of identification algorithms (“taggers”) for hadronically decaying top quarks and W bosons in pp collisions at s = 13 TeV recorded by the ATLAS experiment at the Large Hadron Collider is presented. A set of techniques based on jet shape observables are studied to determine a set of optimal cut-based taggers for use in physics analyses. The studies are extended to assess the utility of combinations of substructure observables as a multivariate tagger using boosted decision trees or deep neural networks in comparison with taggers based on two-variable combinations. In addition, for highly boosted top-quark tagging, a deep neural network based on jet constituent inputs as well as a re-optimisation of the shower deconstruction technique is presented. The performance of these taggers is studied in data collected during 2015 and 2016 corresponding to 36.1 fb - 1 for the tt¯ and γ+ jet and 36.7 fb - 1 for the dijet event topologies. © 2019, CERN for the benefit of the ATLAS collaboration.
publishDate 2019
dc.date.none.fl_str_mv 2019-04-30
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/124624
url http://sedici.unlp.edu.ar/handle/10915/124624
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1434-6044
info:eu-repo/semantics/altIdentifier/issn/1434-6052
info:eu-repo/semantics/altIdentifier/arxiv/1808.07858
info:eu-repo/semantics/altIdentifier/doi/10.1140/epjc/s10052-019-6847-8
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.070432

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