Classifying astronomical sources with machine learning

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dc.contributor.advisorSolanes, José M. (José María)
dc.contributor.advisorSalamó Llorente, Maria
dc.contributor.authorSabatés de la Huerta, Jordi
dc.date.accessioned2022-10-11T11:16:45Z
dc.date.available2022-10-11T11:16:45Z
dc.date.issued2022-02
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2022, Tutors: José Maria Solanes Majúa, Maria Salamó Llorenteca
dc.description.abstractMore than 4 million astronomical sources extracted from the Sloan Digital Sky Survey catalog have been used to train a set of machine learning models, selected with a benchmarking program, in order to identify the best basic classifier of astronomical sources for future observations. We have also applied different filters to our dataset that modify its selection function, measuring the accuracy of the selected model to evaluate under which observational constraints this model performs betterca
dc.format.extent5 p.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2445/189820
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Sabatés, 2022
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.classificationObjecte astronòmiccat
dc.subject.classificationAprenentatge automàticcat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherAstronomical objecteng
dc.subject.otherMachine learningeng
dc.subject.otherBachelor's theseseng
dc.titleClassifying astronomical sources with machine learningeng
dc.typeinfo:eu-repo/semantics/bachelorThesisca

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