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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/151518
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:36:38Zoai: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:34982025-09-29 09:36:38.615CONICET 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 |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |