Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector

Autores
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
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A → BC, for mA ∼ O(TeV), mB,mC ∼O(100  GeV) and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 √s = 13  TeV pp collision dataset of 139  fb⁻¹ recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with mA, mB, and mC. For example, when mA = 3  TeV and mB ≳ 200  GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on mC. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model bosons.
Instituto de Física La Plata
Materia
Física
Hadronic decays
Hadron colliders
pp collisions
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:10915/133816

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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS DetectorAlonso, FranciscoArduh, Francisco AnuarDova, María TeresaHoya, JoaquínMonticelli, Fernando GabrielOrellana, Gonzalo EnriqueWahlberg, Hernán PabloThe ATLAS CollaborationFísicaHadronic decaysHadron colliderspp collisionsThis Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A → BC, for m<sub>A</sub> ∼ O(TeV), m<sub>B</sub>,m<sub>C</sub> ∼O(100  GeV) and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 √s = 13  TeV pp collision dataset of 139  fb⁻¹ recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with m<sub>A</sub>, m<sub>B</sub>, and m<sub>C</sub>. For example, when m<sub>A</sub> = 3  TeV and m<sub>B</sub> ≳ 200  GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on m<sub>C</sub>. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model bosons.Instituto de Física La Plata2020-09info: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/10915/133816enginfo:eu-repo/semantics/altIdentifier/issn/1079-7114info:eu-repo/semantics/altIdentifier/issn/0031-9007info:eu-repo/semantics/altIdentifier/doi/10.1103/physrevlett.125.131801info:eu-repo/semantics/altIdentifier/pmid/33034503info: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:UNLP2025-09-29T11:31:53Zoai:sedici.unlp.edu.ar:10915/133816Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:31:53.324SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
title Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
spellingShingle Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
Alonso, Francisco
Física
Hadronic decays
Hadron colliders
pp collisions
title_short Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
title_full Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
title_fullStr Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
title_full_unstemmed Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
title_sort Dijet Resonance Search with Weak Supervision Using √s = 13 TeV pp Collisions in the ATLAS Detector
dc.creator.none.fl_str_mv Alonso, Francisco
Arduh, Francisco Anuar
Dova, María Teresa
Hoya, Joaquín
Monticelli, Fernando Gabriel
Orellana, Gonzalo Enrique
Wahlberg, Hernán Pablo
The ATLAS Collaboration
author Alonso, Francisco
author_facet 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 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
dc.subject.none.fl_str_mv Física
Hadronic decays
Hadron colliders
pp collisions
topic Física
Hadronic decays
Hadron colliders
pp collisions
dc.description.none.fl_txt_mv This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A → BC, for m<sub>A</sub> ∼ O(TeV), m<sub>B</sub>,m<sub>C</sub> ∼O(100  GeV) and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 √s = 13  TeV pp collision dataset of 139  fb⁻¹ recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with m<sub>A</sub>, m<sub>B</sub>, and m<sub>C</sub>. For example, when m<sub>A</sub> = 3  TeV and m<sub>B</sub> ≳ 200  GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on m<sub>C</sub>. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model bosons.
Instituto de Física La Plata
description This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A → BC, for m<sub>A</sub> ∼ O(TeV), m<sub>B</sub>,m<sub>C</sub> ∼O(100  GeV) and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 √s = 13  TeV pp collision dataset of 139  fb⁻¹ recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with m<sub>A</sub>, m<sub>B</sub>, and m<sub>C</sub>. For example, when m<sub>A</sub> = 3  TeV and m<sub>B</sub> ≳ 200  GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on m<sub>C</sub>. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model bosons.
publishDate 2020
dc.date.none.fl_str_mv 2020-09
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/10915/133816
url http://sedici.unlp.edu.ar/handle/10915/133816
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1079-7114
info:eu-repo/semantics/altIdentifier/issn/0031-9007
info:eu-repo/semantics/altIdentifier/doi/10.1103/physrevlett.125.131801
info:eu-repo/semantics/altIdentifier/pmid/33034503
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)
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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
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