Proučavanje duboko virtualnog komptonskog raspršenja pomoću strojnog učenja

master's thesis

• Title (english): Studying deeply virtual Compton scattering using machine learning
• Author: Marko Cvitković
• Mentor: Krešimir Kumerički (mentor)
• Committee member: Krešimir Kumerički (predsjednik povjerenstva)
• Committee member: Sanjin Benić (član povjerenstva)
• Committee member: Mihael Makek (član povjerenstva)
• Committee member: Nikola Poljak (član povjerenstva)
• Committee member: Davor Horvatić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Physics); Zagreb
• Defense date and country: 2020-10-09, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Physics

Abstract

Duboko virtualno komptonsko raspršenje (DVCS) je proces kojim se dobro može izučavati trodimenzionalna raspodjela kvarkova i gluona u protonu. Funkcije strukture tog procesa (komptonski form faktori, CFF-ovi) su esencijalno neperturbativni objekti koje modeliramo neuronskim mrežama. One predstavljaju optimalan izbor zbog činjenice da ne unose pristranost. U ovom radu, tako generiran model DVCS amplituda prilagođava se različitim eksperimentalnim mjerenjima. Pokazano je da je prilagodba na podatke najkvalitetnija u slučaju kada se koriste svi CFF-ovi u vodećem doprinosu. Uočene su opservable na koje se model kvalitetnije prilagodi u odnosu na druge te su prikazani naučeni CFF-ovi ovisno o izboru značajki neuronske mreže.

Abstract (english)

Deeply virtual Compton scattering (DVCS) is a process which provides access to quality research of three-dimensional distribution of quarks and gluons inside the proton. Structure functions of that process (Compton form factors, CFF) are essentially non-perturbative objects which we model with neural networks. They represent the optimal choice due to the fact that they do not introduce bias. In this thesis, DVCS amplitudes generated that way are fitted to different sets of experimental data. It is shown that using all leading order CFF functions in modeling experimental observables yields best fit to the data. Also, observables which yield better fits to data than the others are notices and learned CFF functions, based on neural network features choices, are shown.

• Keywords: duboko virtualno komptonsko raspršenje; generalizirane partonske distribucije; komptonski form faktori; neuronske mreže
• Keywords (english): deeply virtual Compton scattering; generalized parton distributions; Compton form factors; neural networks
• Language: croatian
• URN:NBN: urn:nbn:hr:217:651085
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2021-01-04 13:07:05