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master's thesis
Potraga za novim česticama pomoću tehnika strojnog učenja
Zagreb: University of Zagreb, Faculty of Science, 2021. urn:nbn:hr:217:601043
Hadži Veljković, Tin
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

Cite this document

Hadži Veljković, T. (2021). Potraga za novim česticama pomoću tehnika strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:601043

Hadži Veljković, Tin. "Potraga za novim česticama pomoću tehnika strojnog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:601043

Hadži Veljković, Tin. "Potraga za novim česticama pomoću tehnika strojnog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:601043

Hadži Veljković, T. (2021). 'Potraga za novim česticama pomoću tehnika strojnog učenja', Master's thesis, University of Zagreb, Faculty of Science, accessed 19 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:601043

Hadži Veljković T. Potraga za novim česticama pomoću tehnika strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2021 [cited 2024 December 19] Available at: https://urn.nsk.hr/urn:nbn:hr:217:601043

T. Hadži Veljković, "Potraga za novim česticama pomoću tehnika strojnog učenja", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:217:601043

Metadata
TitlePotraga za novim česticama pomoću tehnika strojnog učenja
Title (english)The search for new particles with the use of machine learning techniques
AuthorTin Hadži Veljković
MentorNikola Poljak (mentor)
Committee memberNikola Poljak (predsjednik povjerenstva)
Committee memberDavor Horvatić (član povjerenstva)
Committee memberMirko Planinić (član povjerenstva)
Committee memberKrešimir Kumerički (član povjerenstva)
Committee memberIvica Smolić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2021-05-27, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
U ovom radu predstavljene su dvije metode za detekciju novih čestica na sudarivačima poput LHC-a. Obje metode se zasnivaju na uporabi neuralnih mreža, točnije autoenkodera i U-Net mreže. Podaci korišteni u ovom radu dani su u sklopu natjecanja LHC Olympics 2020. Dobivena su dva skupa podataka: podaci koji ne sadrže nove čestice, odnosno opisuju pozadinu i podaci koji sadrže pozadinu i nepoznat broj novih čestica. Treniranjem autoenkoderskih modela na podacima koji sadrže samo pozadinu pokušali smo naučiti njene bitne značajke. Nadalje, evaluacijom modela na drugim podacima pokušali smo detektirati nove čestice tražeći događaje sa lošom autoenkoderskom rekonstrukcijom. Ako događaji s novim česticama odstupaju od pozadine, onda se očekuje da će ih autoenkoder gore rekonstruirati. Uporabom U-Net mreže pokušali smo naučiti model da oponaša anti-kT algoritam grupiranja čestica u mlazove te detektirati nove čestice koristeći istu metodologiju kao i za autoenkoderske modele.
Abstract (english)
In this work we will present the use of machine learning techniques for detecting new particles. Two different types of neural networks were used, namely autoencoders and U-Net. The data used was provided as a part of the competition LHC Olympics 2020. We were given two datasets: a dataset containing only background events and a dataset containing an unknown number of new particles along with the background events. Autoencoder models were trained using the first dataset with the goal of learning crucial background information via latent variables. The new particles are considered as anomalies, and therefore we expect the model to poorly reconstruct the data when evaluated on the events containing new particles. The U-Net model was used to learn the anti-kT jet clustering algorithm, and anomaly detection was conducted using the same methodology as for the autoencoders.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:601043
Study programmeTitle: Physics; specializations in: Research
Course: Research
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: magistar/magistra fizike (magistar/magistra fizike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2021-06-17 12:08:55