Learning the latent structure of collider events

Autores
Dillon, B. M.; Faroughy, D. A.; Kamenik, J. F.; Szewc, Manuel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We pair the model with an approximate inference algorithm called Variational Inference, which we then use to extract the latent probability distributions describing the learned underlying structure of collider events. We provide a detailed systematic study of the technique using two example scenarios to learn the latent structure of di-jet event samples made up of QCD background events and either tt¯.
Fil: Dillon, B. M.. Institute Jo?ef Stefan; Eslovenia
Fil: Faroughy, D. A.. Universitat Zurich; Suiza
Fil: Kamenik, J. F.. Institute Jo?ef Stefan; Eslovenia. University of Ljubljana; Eslovenia
Fil: Szewc, Manuel. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
BEYOND STANDARD MODEL
HADRON-HADRON SCATTERING (EXPERIMENTS)
JET SUBSTRUCTURE
JETS
PARTICLE AND RESONANCE PRODUCTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/151518
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Learning the latent structure of collider eventsDillon, B. M.Faroughy, D. A.Kamenik, J. F.Szewc, ManuelBEYOND STANDARD MODELHADRON-HADRON SCATTERING (EXPERIMENTS)JET SUBSTRUCTUREJETSPARTICLE AND RESONANCE PRODUCTIONhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We pair the model with an approximate inference algorithm called Variational Inference, which we then use to extract the latent probability distributions describing the learned underlying structure of collider events. We provide a detailed systematic study of the technique using two example scenarios to learn the latent structure of di-jet event samples made up of QCD background events and either tt¯.Fil: Dillon, B. M.. Institute Jo?ef Stefan; EsloveniaFil: Faroughy, D. A.. Universitat Zurich; SuizaFil: Kamenik, J. F.. Institute Jo?ef Stefan; Eslovenia. University of Ljubljana; EsloveniaFil: Szewc, Manuel. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaSpringer2020-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/151518Dillon, B. M.; Faroughy, D. A.; Kamenik, J. F.; Szewc, Manuel; Learning the latent structure of collider events; Springer; Journal of High Energy Physics; 2020; 206; 10-2020; 1-481029-8479CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/JHEP10(2020)206info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/JHEP10(2020)206info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:04:44Zoai:ri.conicet.gov.ar:11336/151518instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:04:45.092CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning the latent structure of collider events
title Learning the latent structure of collider events
spellingShingle Learning the latent structure of collider events
Dillon, B. M.
BEYOND STANDARD MODEL
HADRON-HADRON SCATTERING (EXPERIMENTS)
JET SUBSTRUCTURE
JETS
PARTICLE AND RESONANCE PRODUCTION
title_short Learning the latent structure of collider events
title_full Learning the latent structure of collider events
title_fullStr Learning the latent structure of collider events
title_full_unstemmed Learning the latent structure of collider events
title_sort Learning the latent structure of collider events
dc.creator.none.fl_str_mv Dillon, B. M.
Faroughy, D. A.
Kamenik, J. F.
Szewc, Manuel
author Dillon, B. M.
author_facet Dillon, B. M.
Faroughy, D. A.
Kamenik, J. F.
Szewc, Manuel
author_role author
author2 Faroughy, D. A.
Kamenik, J. F.
Szewc, Manuel
author2_role author
author
author
dc.subject.none.fl_str_mv BEYOND STANDARD MODEL
HADRON-HADRON SCATTERING (EXPERIMENTS)
JET SUBSTRUCTURE
JETS
PARTICLE AND RESONANCE PRODUCTION
topic BEYOND STANDARD MODEL
HADRON-HADRON SCATTERING (EXPERIMENTS)
JET SUBSTRUCTURE
JETS
PARTICLE AND RESONANCE PRODUCTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We pair the model with an approximate inference algorithm called Variational Inference, which we then use to extract the latent probability distributions describing the learned underlying structure of collider events. We provide a detailed systematic study of the technique using two example scenarios to learn the latent structure of di-jet event samples made up of QCD background events and either tt¯.
Fil: Dillon, B. M.. Institute Jo?ef Stefan; Eslovenia
Fil: Faroughy, D. A.. Universitat Zurich; Suiza
Fil: Kamenik, J. F.. Institute Jo?ef Stefan; Eslovenia. University of Ljubljana; Eslovenia
Fil: Szewc, Manuel. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspects of the theoretical model that may have given rise to this structure, and can be used to cluster or classify the events for analysis purposes. The unsupervised machine-learning technique is based on the probabilistic (Bayesian) generative model of Latent Dirichlet Allocation. We pair the model with an approximate inference algorithm called Variational Inference, which we then use to extract the latent probability distributions describing the learned underlying structure of collider events. We provide a detailed systematic study of the technique using two example scenarios to learn the latent structure of di-jet event samples made up of QCD background events and either tt¯.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/151518
Dillon, B. M.; Faroughy, D. A.; Kamenik, J. F.; Szewc, Manuel; Learning the latent structure of collider events; Springer; Journal of High Energy Physics; 2020; 206; 10-2020; 1-48
1029-8479
CONICET Digital
CONICET
url http://hdl.handle.net/11336/151518
identifier_str_mv Dillon, B. M.; Faroughy, D. A.; Kamenik, J. F.; Szewc, Manuel; Learning the latent structure of collider events; Springer; Journal of High Energy Physics; 2020; 206; 10-2020; 1-48
1029-8479
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/JHEP10(2020)206
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/JHEP10(2020)206
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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