Learning latent jet structure

Autores
Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Szewc, Manuel
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted tt jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way.
Fil: Dillon, Barry M.. Ruprecht Karls Universitat Heidelberg. Fakultat für Physik and Astronomie; Alemania
Fil: Faroughy, Darius A.. Universitat Zurich; Suiza
Fil: Kamenik, Jernej F.. University of Ljubljana; Eslovenia. Jožef Stefan Institute; Eslovenia
Fil: Szewc, Manuel. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
BAYESIAN SEMI-SUPERVISED LEARNING
JET SUBSTRUCTURE ANALYSIS
QCD
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/154467
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/154467
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Learning latent jet structureDillon, Barry M.Faroughy, Darius A.Kamenik, Jernej F.Szewc, ManuelBAYESIAN SEMI-SUPERVISED LEARNINGJET SUBSTRUCTURE ANALYSISQCDhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted tt jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way.Fil: Dillon, Barry M.. Ruprecht Karls Universitat Heidelberg. Fakultat für Physik and Astronomie; AlemaniaFil: Faroughy, Darius A.. Universitat Zurich; SuizaFil: Kamenik, Jernej F.. University of Ljubljana; Eslovenia. Jožef Stefan Institute; EsloveniaFil: Szewc, Manuel. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaMultidisciplinary Digital Publishing Institute2021-06-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/154467Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Szewc, Manuel; Learning latent jet structure; Multidisciplinary Digital Publishing Institute; Symmetry; 13; 7; 29-6-2021; 1-112073-8994CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3390/sym13071167info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-8994/13/7/1167info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:04:46Zoai:ri.conicet.gov.ar:11336/154467instacron: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 12:04:46.924CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning latent jet structure
title Learning latent jet structure
spellingShingle Learning latent jet structure
Dillon, Barry M.
BAYESIAN SEMI-SUPERVISED LEARNING
JET SUBSTRUCTURE ANALYSIS
QCD
title_short Learning latent jet structure
title_full Learning latent jet structure
title_fullStr Learning latent jet structure
title_full_unstemmed Learning latent jet structure
title_sort Learning latent jet structure
dc.creator.none.fl_str_mv Dillon, Barry M.
Faroughy, Darius A.
Kamenik, Jernej F.
Szewc, Manuel
author Dillon, Barry M.
author_facet Dillon, Barry M.
Faroughy, Darius A.
Kamenik, Jernej F.
Szewc, Manuel
author_role author
author2 Faroughy, Darius A.
Kamenik, Jernej F.
Szewc, Manuel
author2_role author
author
author
dc.subject.none.fl_str_mv BAYESIAN SEMI-SUPERVISED LEARNING
JET SUBSTRUCTURE ANALYSIS
QCD
topic BAYESIAN SEMI-SUPERVISED LEARNING
JET SUBSTRUCTURE ANALYSIS
QCD
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 summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted tt jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way.
Fil: Dillon, Barry M.. Ruprecht Karls Universitat Heidelberg. Fakultat für Physik and Astronomie; Alemania
Fil: Faroughy, Darius A.. Universitat Zurich; Suiza
Fil: Kamenik, Jernej F.. University of Ljubljana; Eslovenia. Jožef Stefan Institute; Eslovenia
Fil: Szewc, Manuel. Universidad Nacional de San Martín; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation. Using a mixed sample of QCD and boosted tt jet events and focusing on the primary Lund plane observable basis for event measurements, we show how using educated priors on the latent distributions allows to infer on the underlying physical processes in a semi-supervised way.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-29
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/11336/154467
Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Szewc, Manuel; Learning latent jet structure; Multidisciplinary Digital Publishing Institute; Symmetry; 13; 7; 29-6-2021; 1-11
2073-8994
CONICET Digital
CONICET
url http://hdl.handle.net/11336/154467
identifier_str_mv Dillon, Barry M.; Faroughy, Darius A.; Kamenik, Jernej F.; Szewc, Manuel; Learning latent jet structure; Multidisciplinary Digital Publishing Institute; Symmetry; 13; 7; 29-6-2021; 1-11
2073-8994
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/sym13071167
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-8994/13/7/1167
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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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