Classifying astronomical sources with machine learning

Sabatés de la Huerta, Jordi

Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/189820

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DC Field	Value	Language
dc.contributor.advisor	Solanes, José M. (José María)	-
dc.contributor.advisor	Salamó Llorente, Maria	-
dc.contributor.author	Sabatés de la Huerta, Jordi	-
dc.date.accessioned	2022-10-11T11:16:45Z	-
dc.date.available	2022-10-11T11:16:45Z	-
dc.date.issued	2022-02	-
dc.identifier.uri	http://hdl.handle.net/2445/189820	-
dc.description	Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2022, Tutors: José Maria Solanes Majúa, Maria Salamó Llorente	ca
dc.description.abstract	More 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 better	ca
dc.format.extent	5 p.	-
dc.format.mimetype	application/pdf	-
dc.language.iso	eng	ca
dc.rights	cc-by-nc-nd (c) Sabatés, 2022	-
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.source	Treballs Finals de Grau (TFG) - Física	-
dc.subject.classification	Objecte astronòmic	cat
dc.subject.classification	Aprenentatge automàtic	cat
dc.subject.classification	Treballs de fi de grau	cat
dc.subject.other	Astronomical object	eng
dc.subject.other	Machine learning	eng
dc.subject.other	Bachelor's theses	eng
dc.title	Classifying astronomical sources with machine learning	eng
dc.type	info:eu-repo/semantics/bachelorThesis	ca
dc.rights.accessRights	info:eu-repo/semantics/openAccess	ca
Appears in Collections:	Treballs Finals de Grau (TFG) - Física

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