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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/133816
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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 |
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dc.language.none.fl_str_mv |
eng |
language |
eng |
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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 |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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