Machine Learning for Infinite Neutron Matter at Finite Temperatures

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dc.contributor.advisorRios Huguet, Arnau
dc.contributor.advisorRozalén Sarmiento, Javier
dc.contributor.authorMartíınez Marimon, Alba
dc.date.accessioned2024-10-15T15:23:00Z
dc.date.available2024-10-15T15:23:00Z
dc.date.issued2024-06
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2024, Tutors: Arnau Rios Huguet, Javier Rozalén Sarmientoca
dc.description.abstractIn this paper, machine learning is used to model the imaginary part of the self-energy of a neutron-star binary system, which is approximated as infinite neutron matter. The performance of the model is evaluated by computing physical properties such as the momentum distribution and the total energy. The results obtained with the model show a high degree of accuracy when compared to theoretical valuesca
dc.format.extent5 p.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2445/215795
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Martínez, 2024
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Física
dc.subject.classificationAprenentatge automàticcat
dc.subject.classificationEstels de neutronscat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherMachine learningeng
dc.subject.otherNeutron starseng
dc.subject.otherBachelor's theseseng
dc.titleMachine Learning for Infinite Neutron Matter at Finite Temperatureseng
dc.typeinfo:eu-repo/semantics/bachelorThesisca

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