Towards a method to anticipate dark matter signals with deep learning at the LHC

Autores
Arganda Carreras, Ernesto; Medina, Anibal Damian; Perez, Andres Daniel; Szynkman, Alejandro Andrés
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of S/pB, for reasonably large B, where S and B are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.
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
Fil: Medina, Anibal Damian. 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: 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: Szynkman, Alejandro Andrés. 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
Materia
Machine Learning
Dark Matter
LHC Phenomenology
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:11336/212387
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Towards a method to anticipate dark matter signals with deep learning at the LHCArganda Carreras, ErnestoMedina, Anibal DamianPerez, Andres DanielSzynkman, Alejandro AndrésMachine LearningDark MatterLHC Phenomenologyhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of S/pB, for reasonably large B, where S and B are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.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; EspañaFil: Medina, Anibal Damian. 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: 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: Szynkman, Alejandro Andrés. 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; ArgentinaSciPost Foundation2021-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/212387Arganda Carreras, Ernesto; Medina, Anibal Damian; Perez, Andres Daniel; Szynkman, Alejandro Andrés; Towards a method to anticipate dark matter signals with deep learning at the LHC; SciPost Foundation; SciPost Physics; 12; 2; 9-2021; 1-472542-4653CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2105.12018info:eu-repo/semantics/altIdentifier/url/https://scipost.org/10.21468/SciPostPhys.12.2.063info:eu-repo/semantics/altIdentifier/doi/10.21468/SciPostPhys.12.2.063info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:33:00Zoai:ri.conicet.gov.ar:11336/212387instacron: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 09:33:00.938CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Towards a method to anticipate dark matter signals with deep learning at the LHC
title Towards a method to anticipate dark matter signals with deep learning at the LHC
spellingShingle Towards a method to anticipate dark matter signals with deep learning at the LHC
Arganda Carreras, Ernesto
Machine Learning
Dark Matter
LHC Phenomenology
title_short Towards a method to anticipate dark matter signals with deep learning at the LHC
title_full Towards a method to anticipate dark matter signals with deep learning at the LHC
title_fullStr Towards a method to anticipate dark matter signals with deep learning at the LHC
title_full_unstemmed Towards a method to anticipate dark matter signals with deep learning at the LHC
title_sort Towards a method to anticipate dark matter signals with deep learning at the LHC
dc.creator.none.fl_str_mv Arganda Carreras, Ernesto
Medina, Anibal Damian
Perez, Andres Daniel
Szynkman, Alejandro Andrés
author Arganda Carreras, Ernesto
author_facet Arganda Carreras, Ernesto
Medina, Anibal Damian
Perez, Andres Daniel
Szynkman, Alejandro Andrés
author_role author
author2 Medina, Anibal Damian
Perez, Andres Daniel
Szynkman, Alejandro Andrés
author2_role author
author
author
dc.subject.none.fl_str_mv Machine Learning
Dark Matter
LHC Phenomenology
topic Machine Learning
Dark Matter
LHC Phenomenology
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 study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of S/pB, for reasonably large B, where S and B are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.
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
Fil: Medina, Anibal Damian. 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: 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: Szynkman, Alejandro Andrés. 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
description We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of S/pB, for reasonably large B, where S and B are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.
publishDate 2021
dc.date.none.fl_str_mv 2021-09
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/11336/212387
Arganda Carreras, Ernesto; Medina, Anibal Damian; Perez, Andres Daniel; Szynkman, Alejandro Andrés; Towards a method to anticipate dark matter signals with deep learning at the LHC; SciPost Foundation; SciPost Physics; 12; 2; 9-2021; 1-47
2542-4653
CONICET Digital
CONICET
url http://hdl.handle.net/11336/212387
identifier_str_mv Arganda Carreras, Ernesto; Medina, Anibal Damian; Perez, Andres Daniel; Szynkman, Alejandro Andrés; Towards a method to anticipate dark matter signals with deep learning at the LHC; SciPost Foundation; SciPost Physics; 12; 2; 9-2021; 1-47
2542-4653
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2105.12018
info:eu-repo/semantics/altIdentifier/url/https://scipost.org/10.21468/SciPostPhys.12.2.063
info:eu-repo/semantics/altIdentifier/doi/10.21468/SciPostPhys.12.2.063
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
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv SciPost Foundation
publisher.none.fl_str_mv SciPost Foundation
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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