Quantum convolutional neural networks for high energy physics

Cervantes Guevara, Luis Roberto

Quantum convolutional neural networks for high energy physics

dc.audience	generalPublic	es_MX
dc.contributor	Pedraza Morales, María Isabel
dc.contributor.author	Cervantes Guevara, Luis Roberto
dc.date.accessioned	2022-11-15T21:56:54Z
dc.date.available	2022-11-15T21:56:54Z
dc.date.issued	2022-09-05
dc.description.abstract	"Artificial Intelligence is a tool that is increasingly becoming an integral element of scientific research. High Energy Physics, whose experiments produce some of the largest amounts of data in science, is no exception. For this reason, the objective of this thesis is to develop a pipeline for classifying backgrounds and signals particle jets using Machine Learning (ML) techniques, specifically convolutional neural networks (CNN). Particle jets have proven to be a very powerful tool for studying particle collisions at accelerators such as CMS and ATLAS, at the LHC, where the constituents of these events hadronize or decay so quickly that they are very difficult to detect. Jets are objects that seek to retrieve information about these particles by encapsulating the energy depositions that were indeed sensed by the detector. So having an algorithm that can reliably tell us which particle generated a given jet is not a straightforward assignment, even more so taking into account that many approaches can be taken to this problem depending on the way in which we believe it is most convenient to arrange our data".	es_MX
dc.folio	20220906135353-6025-TL	es_MX
dc.format	pdf	es_MX
dc.identificator	1	es_MX
dc.identifier.uri	https://hdl.handle.net/20.500.12371/16897
dc.language.iso	eng	es_MX
dc.matricula.creator	201764607	es_MX
dc.publisher	Benemérita Universidad Autónoma de Puebla	es_MX
dc.rights.acces	openAccess	es_MX
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0	es_MX
dc.subject.classification	CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA	es_MX
dc.subject.lcc	Partículas (Física nuclear)--Investigación	es_MX
dc.subject.lcc	Aceleradores de partículas	es_MX
dc.subject.lcc	Colisiones (Física nuclear)--Procesamiento de datos	es_MX
dc.subject.lcc	Inteligencia artificial	es_MX
dc.subject.lcc	Aprendizaje automático	es_MX
dc.subject.lcc	Redes neuronales (Computación)	es_MX
dc.subject.lcc	Algoritmos computacionales	es_MX
dc.thesis.career	Licenciatura en Física	es_MX
dc.thesis.degreediscipline	Área de Ingeniería y Ciencias Exactas	es_MX
dc.thesis.degreegrantor	Facultad de Ciencias Físico Matemáticas	es_MX
dc.thesis.degreetoobtain	Licenciado (a) en Física	es_MX
dc.title	Quantum convolutional neural networks for high energy physics	es_MX
dc.type	Tesis de licenciatura	es_MX
dc.type.conacyt	bachelorThesis	es_MX
dc.type.degree	Licenciatura	es_MX