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master's thesis
Potraga za pulsarima primjenom strojnog učenja
Zagreb: University of Zagreb, Faculty of Science, 2020. urn:nbn:hr:217:327347
Bilić, Marin
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

Cite this document

Bilić, M. (2020). Potraga za pulsarima primjenom strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:327347

Bilić, Marin. "Potraga za pulsarima primjenom strojnog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2020. https://urn.nsk.hr/urn:nbn:hr:217:327347

Bilić, Marin. "Potraga za pulsarima primjenom strojnog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2020. https://urn.nsk.hr/urn:nbn:hr:217:327347

Bilić, M. (2020). 'Potraga za pulsarima primjenom strojnog učenja', Master's thesis, University of Zagreb, Faculty of Science, accessed 14 May 2024, https://urn.nsk.hr/urn:nbn:hr:217:327347

Bilić M. Potraga za pulsarima primjenom strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2020 [cited 2024 May 14] Available at: https://urn.nsk.hr/urn:nbn:hr:217:327347

M. Bilić, "Potraga za pulsarima primjenom strojnog učenja", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:217:327347

Metadata
TitlePotraga za pulsarima primjenom strojnog učenja
Title (english)Search for pulsars using machine learning
AuthorMarin Bilić
MentorGoranka Bilalbegović (mentor)
Committee memberGoranka Bilalbegović (predsjednik povjerenstva)
Committee memberKrešimir Kumerički (član povjerenstva)
Committee memberMihael Makek (član povjerenstva)
Committee memberIvica Smolić (član povjerenstva)
Committee memberDalibor Paar (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2020-09-22, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
Pulsari su brzo rotirajuće neutronske zvijezde. Ti vrlo zanimljivi objekti fasciniraju astronome još od trenutka kad su prvi put pronađeni. S vremenom su teleskopi postajali sve bolji te se tako i broj otkrivenih pulsara povećavao. Ručno analiziranje podataka dobivenih pomoću teleskopa u potrazi za pulsarima je vrlo naporan i monoton proces koji zahtjeva puno vremena. Međutim, razvojem strojnog učenja je ovaj zadatak postao rješiv pomoću računala. U ovom diplomskom radu analizirani su pripremljeni podaci s kandidatima za pulsare. Njihovom obradom pomoću tri različita algoritma strojnog učenja razvijeni su modeli kategorizacije koji daju veliku preciznost. Korišteni su algoritmi k-najbližih susjeda, potpornih vektora i stabla odlučivanja. Ti algoritmi su analizirani i testirani za više vrijednosti parametara kako bi se dobila najbolja rješenja. Uspoređeni su rezultati primijenjenih algoritama. U metodičkom dijelu je dan osvrt na teme iz astronomije i pulsara u osnovnim i srednjim školama, te je razrađen mogući uvod u strojno učenje za srednjoškolsku nastavu.
Abstract (english)
Pulsars are neutron stars with fast rotation speeds. These very interesting objects fascinate astronomers from the moment they were discovered. With time, telescopes are becoming better, therefore the number of discovered pulsars is growing. Analyzing the telescope data by hand is a very tedious and monotone process. However, with development of machine learning this task became easily solvable by using a computer. In this diploma thesis we analyze the prepared data with pulsar candidates. Three different machine learning algorithms are applied and they produced prediction models with high accuracy. Algorithms applied are: k-nearest neighbors, support vectors and decision tree. All algorithms are analyzed and tested for several values of parameters in order to produce the best solution. Results of applied algorithms are compared. In the methodical section, a summary of topics in astronomy and pulsars for middle and high school students is given. In addition, a possible introduction to machine learning for high school students is formulated.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:327347
Study programmeTitle: Physics and Computer Science; specializations in: Education
Course: Education
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: magistar/magistra fizike i informatike (magistar/magistra fizike i informatike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-01-19 09:56:52