Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion

Descripción del Articulo

21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023
Detalles Bibliográficos
Autores: Sánchez, Luis, Díaz, Félix, Rojas, Jhonny
Formato: objeto de conferencia
Fecha de Publicación:2023
Institución:Universidad Tecnológica del Perú
Repositorio:UTP-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.utp.edu.pe:20.500.12867/10766
Enlace del recurso:https://hdl.handle.net/20.500.12867/10766
https://dx.doi.org/10.18687/LACCEI2023.1.1.1072
Nivel de acceso:acceso abierto
Materia:Higgs boson
Vector boson fusion
Standard Model (SM)
DNN analysis
https://purl.org/pe-repo/ocde/ford#5.03.01
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dc.title.es_PE.fl_str_mv Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
title Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
spellingShingle Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
Sánchez, Luis
Higgs boson
Vector boson fusion
Standard Model (SM)
DNN analysis
https://purl.org/pe-repo/ocde/ford#5.03.01
title_short Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
title_full Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
title_fullStr Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
title_full_unstemmed Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
title_sort Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
author Sánchez, Luis
author_facet Sánchez, Luis
Díaz, Félix
Rojas, Jhonny
author_role author
author2 Díaz, Félix
Rojas, Jhonny
author2_role author
author
dc.contributor.author.fl_str_mv Sánchez, Luis
Díaz, Félix
Rojas, Jhonny
dc.subject.es_PE.fl_str_mv Higgs boson
Vector boson fusion
Standard Model (SM)
DNN analysis
topic Higgs boson
Vector boson fusion
Standard Model (SM)
DNN analysis
https://purl.org/pe-repo/ocde/ford#5.03.01
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.03.01
description 21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023
publishDate 2023
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dc.date.available.none.fl_str_mv 2025-01-24T17:19:38Z
dc.date.issued.fl_str_mv 2023
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dc.identifier.issn.none.fl_str_mv 2414-6390
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dc.identifier.journal.es_PE.fl_str_mv Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
dc.identifier.doi.none.fl_str_mv https://dx.doi.org/10.18687/LACCEI2023.1.1.1072
identifier_str_mv 9786289520743
2414-6390
Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
url https://hdl.handle.net/20.500.12867/10766
https://dx.doi.org/10.18687/LACCEI2023.1.1.1072
dc.language.iso.es_PE.fl_str_mv eng
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dc.relation.ispartofseries.none.fl_str_mv Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology;Vol. 2023-July
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dc.publisher.es_PE.fl_str_mv Latin American and Caribbean Consortium of Engineering Institutions
dc.publisher.country.es_PE.fl_str_mv US
dc.source.es_PE.fl_str_mv Repositorio Institucional - UTP
Universidad Tecnológica del Perú
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spelling Sánchez, LuisDíaz, FélixRojas, Jhonny2025-01-24T17:19:38Z2025-01-24T17:19:38Z202397862895207432414-6390https://hdl.handle.net/20.500.12867/10766Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technologyhttps://dx.doi.org/10.18687/LACCEI2023.1.1.107221st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023In this article, an analysis of the Higgs boson production via vector boson fusion in the SM I-WW- 2l2v (l = e, u) is performed from an optimization techniquc in the event selection, called DNN analysis. This analysis compares the Standard selection process that CERN performs to study the production of a particle from a cut-based analysis, where the study of statistical significance shows that DNN analysis can better separate signal and background events. To perform the DNN analysis, we optimized the neural network configuration to discriminate ignal and background events effectively. Moreover, studies of activation functions such as RELU and Sigmoid, stochastic optimization methods such as ADAM, and regularization methods such as Dropout. All this leads to constructing an optimal neural network topology capable of learning events and signal and background discrimination. Finally, we found an important improvement of approximately 47% and 27% for ZyBf and ZHiggs respectively.Escuela de Postgradoapplication/pdfengLatin American and Caribbean Consortium of Engineering InstitutionsUSProceedings of the LACCEI international Multi-conference for Engineering, Education and Technology;Vol. 2023-Julyinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/Repositorio Institucional - UTPUniversidad Tecnológica del Perúreponame:UTP-Institucionalinstname:Universidad Tecnológica del Perúinstacron:UTPHiggs bosonVector boson fusionStandard Model (SM)DNN analysishttps://purl.org/pe-repo/ocde/ford#5.03.01Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionORIGINALL.Sanchez_F.Diaz_J.Rojas_Conference_Paper_2023.pdfL.Sanchez_F.Diaz_J.Rojas_Conference_Paper_2023.pdfapplication/pdf945531https://repositorio.utp.edu.pe/backend/api/core/bitstreams/e01adf0d-5d95-4ea3-8265-8532b3a84907/downloadba9ab5ade8b985c11cbdacf58fa59796MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.utp.edu.pe/backend/api/core/bitstreams/ff72e66e-e614-4b95-9ae9-3882f8afa24f/download8a4605be74aa9ea9d79846c1fba20a33MD52TEXTL.Sanchez_F.Diaz_J.Rojas_Conference_Paper_2023.pdf.txtL.Sanchez_F.Diaz_J.Rojas_Conference_Paper_2023.pdf.txtExtracted texttext/plain41764https://repositorio.utp.edu.pe/backend/api/core/bitstreams/72879a44-c16c-4c2d-a850-b2a5fb0d8932/downloadbae48dc0ec890c37f6ef7c685045fcf0MD55THUMBNAILL.Sanchez_F.Diaz_J.Rojas_Conference_Paper_2023.pdf.jpgL.Sanchez_F.Diaz_J.Rojas_Conference_Paper_2023.pdf.jpgGenerated Thumbnailimage/jpeg55706https://repositorio.utp.edu.pe/backend/api/core/bitstreams/606cff91-4613-4247-a7cb-48f4c94d11a3/download0826d1f414c88503ea7fcb8d251ce21dMD5620.500.12867/10766oai:repositorio.utp.edu.pe:20.500.12867/107662025-11-30 17:54:51.985https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessopen.accesshttps://repositorio.utp.edu.peRepositorio de la Universidad Tecnológica del Perúrepositorio@utp.edu.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Deep Neural Network to Describe the Measurement of the Higgs Production in the Full Leptonic Channel via Vector Boson Fusion
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