Imposing exclusion limits on new physics with machine-learned likelihoods

Autores
Arganda Carreras, Ernesto; de Los Rios, Martín Emilio; Perez, Andres Daniel; Sandá Seoane, Rosa María
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here we extend the MLL method by including the exclusion hypothesis tests and study it first on a toy model of multivariate Gaussian distributions, where the true probability distribution functions are known. We then apply it to a case of interest in the search for new physics at the LHC, in which a ′ boson decays into lepton pairs, comparing the performance of MLL for estimating 95% CL exclusion limits with respect to the prospects reported by ATLAS at 14 TeV with a luminosity of 3 ab−1.
Fil: Arganda Carreras, Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; España
Fil: de Los Rios, Martín Emilio. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; España
Fil: Perez, Andres Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
Fil: Sandá Seoane, Rosa María. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; España
Materia
BSM PHENOMENOLOGY
COLLIDER PHYSICS
LHC
MACHINE LEARNING
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:11336/213847
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Imposing exclusion limits on new physics with machine-learned likelihoodsArganda Carreras, Ernestode Los Rios, Martín EmilioPerez, Andres DanielSandá Seoane, Rosa MaríaBSM PHENOMENOLOGYCOLLIDER PHYSICSLHCMACHINE LEARNINGhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here we extend the MLL method by including the exclusion hypothesis tests and study it first on a toy model of multivariate Gaussian distributions, where the true probability distribution functions are known. We then apply it to a case of interest in the search for new physics at the LHC, in which a ′ boson decays into lepton pairs, comparing the performance of MLL for estimating 95% CL exclusion limits with respect to the prospects reported by ATLAS at 14 TeV with a luminosity of 3 ab−1.Fil: Arganda Carreras, Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; EspañaFil: de Los Rios, Martín Emilio. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; EspañaFil: Perez, Andres Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Sandá Seoane, Rosa María. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; EspañaSissa2022-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/213847Arganda Carreras, Ernesto; de Los Rios, Martín Emilio; Perez, Andres Daniel; Sandá Seoane, Rosa María; Imposing exclusion limits on new physics with machine-learned likelihoods; Sissa; Proceedings of Science; 2022; 10-2022; 1226-12321824-8039CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.22323/1.414.1226info:eu-repo/semantics/altIdentifier/url/https://pos.sissa.it/414/1226/pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:12Zoai:ri.conicet.gov.ar:11336/213847instacron: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 09:33:13.163CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Imposing exclusion limits on new physics with machine-learned likelihoods
title Imposing exclusion limits on new physics with machine-learned likelihoods
spellingShingle Imposing exclusion limits on new physics with machine-learned likelihoods
Arganda Carreras, Ernesto
BSM PHENOMENOLOGY
COLLIDER PHYSICS
LHC
MACHINE LEARNING
title_short Imposing exclusion limits on new physics with machine-learned likelihoods
title_full Imposing exclusion limits on new physics with machine-learned likelihoods
title_fullStr Imposing exclusion limits on new physics with machine-learned likelihoods
title_full_unstemmed Imposing exclusion limits on new physics with machine-learned likelihoods
title_sort Imposing exclusion limits on new physics with machine-learned likelihoods
dc.creator.none.fl_str_mv Arganda Carreras, Ernesto
de Los Rios, Martín Emilio
Perez, Andres Daniel
Sandá Seoane, Rosa María
author Arganda Carreras, Ernesto
author_facet Arganda Carreras, Ernesto
de Los Rios, Martín Emilio
Perez, Andres Daniel
Sandá Seoane, Rosa María
author_role author
author2 de Los Rios, Martín Emilio
Perez, Andres Daniel
Sandá Seoane, Rosa María
author2_role author
author
author
dc.subject.none.fl_str_mv BSM PHENOMENOLOGY
COLLIDER PHYSICS
LHC
MACHINE LEARNING
topic BSM PHENOMENOLOGY
COLLIDER PHYSICS
LHC
MACHINE LEARNING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here we extend the MLL method by including the exclusion hypothesis tests and study it first on a toy model of multivariate Gaussian distributions, where the true probability distribution functions are known. We then apply it to a case of interest in the search for new physics at the LHC, in which a ′ boson decays into lepton pairs, comparing the performance of MLL for estimating 95% CL exclusion limits with respect to the prospects reported by ATLAS at 14 TeV with a luminosity of 3 ab−1.
Fil: Arganda Carreras, Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; España
Fil: de Los Rios, Martín Emilio. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; España
Fil: Perez, Andres Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentina
Fil: Sandá Seoane, Rosa María. Consejo Superior de Investigaciones Científicas; España. Universidad Autónoma de Madrid; España
description Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here we extend the MLL method by including the exclusion hypothesis tests and study it first on a toy model of multivariate Gaussian distributions, where the true probability distribution functions are known. We then apply it to a case of interest in the search for new physics at the LHC, in which a ′ boson decays into lepton pairs, comparing the performance of MLL for estimating 95% CL exclusion limits with respect to the prospects reported by ATLAS at 14 TeV with a luminosity of 3 ab−1.
publishDate 2022
dc.date.none.fl_str_mv 2022-10
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/11336/213847
Arganda Carreras, Ernesto; de Los Rios, Martín Emilio; Perez, Andres Daniel; Sandá Seoane, Rosa María; Imposing exclusion limits on new physics with machine-learned likelihoods; Sissa; Proceedings of Science; 2022; 10-2022; 1226-1232
1824-8039
CONICET Digital
CONICET
url http://hdl.handle.net/11336/213847
identifier_str_mv Arganda Carreras, Ernesto; de Los Rios, Martín Emilio; Perez, Andres Daniel; Sandá Seoane, Rosa María; Imposing exclusion limits on new physics with machine-learned likelihoods; Sissa; Proceedings of Science; 2022; 10-2022; 1226-1232
1824-8039
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.22323/1.414.1226
info:eu-repo/semantics/altIdentifier/url/https://pos.sissa.it/414/1226/pdf
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 Sissa
publisher.none.fl_str_mv Sissa
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 13.070432

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