Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC

ilustraciones, diagramas, fotografías

Autores:
Vinasco Soler, Rafael Andrei
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/85588
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/85588
https://repositorio.unal.edu.co/
Palabra clave:
530 - Física
500 - Ciencias naturales y matemáticas
W-tagging
Lund Jet plane
Machine learning
Identificación bosones W
Plano de Lund
Aprendizaje automático
Higgs boson
bosón de Higgs
W boson
bosón W
Rights
openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_abe94b522a7e689982b05e24875c9b0e
oai_identifier_str oai:repositorio.unal.edu.co:unal/85588
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
dc.title.translated.eng.fl_str_mv Use of the Lund plane technique for the identification of particles in ATLAS experiment at the LHC
title Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
spellingShingle Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
530 - Física
500 - Ciencias naturales y matemáticas
W-tagging
Lund Jet plane
Machine learning
Identificación bosones W
Plano de Lund
Aprendizaje automático
Higgs boson
bosón de Higgs
W boson
bosón W
title_short Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_full Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_fullStr Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_full_unstemmed Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_sort Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
dc.creator.fl_str_mv Vinasco Soler, Rafael Andrei
dc.contributor.advisor.none.fl_str_mv Sandoval Usme, Carlos Eduardo
dc.contributor.author.none.fl_str_mv Vinasco Soler, Rafael Andrei
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Partículas Fenyx-Un
dc.subject.ddc.spa.fl_str_mv 530 - Física
500 - Ciencias naturales y matemáticas
topic 530 - Física
500 - Ciencias naturales y matemáticas
W-tagging
Lund Jet plane
Machine learning
Identificación bosones W
Plano de Lund
Aprendizaje automático
Higgs boson
bosón de Higgs
W boson
bosón W
dc.subject.proposal.eng.fl_str_mv W-tagging
Lund Jet plane
Machine learning
dc.subject.proposal.spa.fl_str_mv Identificación bosones W
Plano de Lund
Aprendizaje automático
dc.subject.wikidata.none.fl_str_mv Higgs boson
bosón de Higgs
W boson
bosón W
description ilustraciones, diagramas, fotografías
publishDate 2023
dc.date.issued.none.fl_str_mv 2023-07-28
dc.date.accessioned.none.fl_str_mv 2024-02-01T16:13:37Z
dc.date.available.none.fl_str_mv 2024-02-01T16:13:37Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/85588
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/85588
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.license.spa.fl_str_mv Reconocimiento 4.0 Internacional
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dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ciencias - Maestría en Ciencias - Física
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
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spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sandoval Usme, Carlos Eduardo1fb44a8317231208c66f0f92600dbfd2Vinasco Soler, Rafael Andrei765e2d0f9ec9dfd2b448a01d24a3b3bfGrupo de Partículas Fenyx-Un2024-02-01T16:13:37Z2024-02-01T16:13:37Z2023-07-28https://repositorio.unal.edu.co/handle/unal/85588Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografíasSe estudia el uso de las variables del Plano de Lund como entradas para algoritmos de aprendizaje automático basados en Graph neural Networks (GNN) para la identificación de jets provenientes de bosones W y bosones de Higgs con alto momento transverso en el experimento del ATLAS. Se realizó el análisis por separado para bosones W y para bosones de Higgs. Para bosones W se probaron 5 diferentes arquitecturas de GNN, además para la arquitectura con mejor comportamiento se estudia la inclusión de una red adversaria con el propósito de obtener un identificador de partículas no dependiente de la masa. Para el bosón de Higgs se realizaron los estudios con el propósito de detectar únicamente el canal de decaimiento H a bb, para este caso 2 arquitecturas fueron estudiadas, una siendo un modelo basado únicamente en GNN que usa variables del plano de Lund y otra un modelo mixto que usa las variables del plano de Lund y además el puntaje del identificador actualmente usado en el experimento del ATLAS. Se obtuvo como resultado para los identificadores de bosones W que 4 de ellos tienen un rechazo de jets no deseados mayor a los identificadores encontrados en la literatura actualmente usados, además para el clasificador entrenado junto a una red adversaria se obtuvo una excelente decorrelación además de mantener un rechazo de eventos no deseados de más del doble en comparación al tagger decorrelacionado de la masa reportado en la literatura. Para el caso de identificación de bosones de Higgs se encontró un pobre rendimiento del algoritmo que únicamente usa las variables del Plano de Lund, pero para el algoritmo mixto se obtiene una mejora de alrededor de un orden de magnitud en el rechazo de los jets no deseados. Para el clasificador mixto entrenado junto a una red adversaria se obtuvo una decorrelación buena con un rechazo de jets de no deseados 3 veces mayor al tagger actualmente usado reportado.The use of the Lund Plane variables as inputs for machine learning algorithms based on Graph neural Networks (GNN) for the identification of jets coming from W bosons and Higgs bosons with high transverse moment in the ATLAS experiment is studied. The analisis was performed separately for W bosons and for Higgs bosons. For W bosons, 5 different GNN architectures were tested, for the architecture with the best rejection of unwanted jets the inclusion of an adversarial network during the training is studied, in order to obtain a tagger that does not depend on mass. For the Higgs boson, the studies are done with the purpose of tag only the decay channel H to bb, for this case 2 architectures were studied, one being a model based on GNN that uses variables of the Lund plane and the other proposed is a mixed model that uses the Lund plane variables and also the score of the tagger currently used in the ATLAS experiment. It was obtained for the W boson taggers that 4 of the taggers proposed have a greater rejection of unwanted jets than the currently taggers used found in the literature. In addition, the tagger trained together with an adversarial network obtain an excellent decorrelation with a rejection of unwanted jets of more tan double compared to the mass decorrelated taggers reported in the literature. For the case of Higgs boson identification, a poor performance of the algorithm that only uses the variables of the Lund Plane was found compared to current tagger, but for the mixed algorithm an improvement of around an order of magnitude is obtained in the rejection of unwanted jets. For the classifier trained together with an adversarial network a good decorrelation is obtained, with a rejection of unwanted jets 3 times higher than the currently used tagger reported.MaestríaMagíster en Ciencias - Físicaxi, 110 paginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - FísicaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá530 - Física500 - Ciencias naturales y matemáticasW-taggingLund Jet planeMachine learningIdentificación bosones WPlano de LundAprendizaje automáticoHiggs bosonbosón de HiggsW bosonbosón WUso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHCUse of the Lund plane technique for the identification of particles in ATLAS experiment at the LHCTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMC. S. Wu, E. Ambler, R. W. Hayward, D. D. 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Technical report, CERN, Geneva, 2023. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATLPHYS- PUB-2023-017.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85588/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1030646597.2023.pdf1030646597.2023.pdfTesis de Maestría en Físicaapplication/pdf16076315https://repositorio.unal.edu.co/bitstream/unal/85588/2/1030646597.2023.pdfd629c031f303cd8a21a588707490f292MD52THUMBNAIL1030646597.2023.pdf.jpg1030646597.2023.pdf.jpgGenerated Thumbnailimage/jpeg5154https://repositorio.unal.edu.co/bitstream/unal/85588/3/1030646597.2023.pdf.jpg7ce02814e5b1cde0fa4f604321c99d9dMD53unal/85588oai:repositorio.unal.edu.co:unal/855882024-08-22 23:10:31.121Repositorio Institucional Universidad Nacional de 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