Istraživanje kvarkovsko-gluonske strukture protona pomoću strojnog učenja

master's thesis

• Title (english): Study of quark-gluon structure of proton using machine learning
• Author: Ivan Ćorić
• Mentor: Krešimir Kumerički (mentor)
• Committee member: Krešimir Kumerički (predsjednik povjerenstva)
• Committee member: Nikola Poljak (član povjerenstva)
• Committee member: Davor Horvatić (član povjerenstva)
• Committee member: Ivica Smolić (član povjerenstva)
• Committee member: Matko Milin (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Physics); Zagreb
• Defense date and country: 2019-07-17, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Physics

Abstract

U ovom radu promatrat će se mogućnost ekstrakcije komptonskih form faktora (CFF funkcija) iz tipičnih opservabla mjerenih u procesima leptoprodukcije fotona, gdje su CFF funkcije modelirane neuronskim mrežama. Kako bi se provjerilo hoće li postupak učenja CFF funkcija neuronskim mrežama biti uspješan i o čemu ovisi kvalitetna ekstrakcija, generirani su umjetni eksperimentalni podaci (opservable na koje je dodan šum) iz poznatog Goloskokov-Kroll modela za CFF funkcije. Pokazano je da se uz... More dovoljan broj podataka, CFF funkcije mogu naučiti, da je kvaliteta ekstrakcije to bolja što ima više podataka te su uočene opservable čije uključivanje u skup podataka za učenje poboljšava kvalitetu ekstrakcije u većoj mjeri nego uključivanje drugih opservabli. Less

Abstract (english)

In this thesis we will study a possibility of extraction of Compton form factors (CFFs) from typical observables measured in the process of leptoproduction of photon, where the CFF functions will be modeled with neural networks. In order to verify the validity of learning CFF functions using neural networks and to determine what makes a quality extraction, artificial (mock) experimental data (observables to which noise was applied) was generated from popular Goloskokov-Kroll model for... More CFF functions. It is shown that with the enough data, CFF functions can be learned, that the quality of extraction is getting better when increasing the number of data and some observables were noticed which had more effect on the quality of extraction than the others. Less

• Keywords: generalizirane partonske distribucije; duboko virtualno komptonsko raspršenje; Komptonski form faktori; neuronske mreže
• Keywords (english): generalized parton distributions; deeply virtual compton scattering; Compton form factors; neural networks
• Language: croatian
• URN:NBN: urn:nbn:hr:217:856388
• 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: 2019-09-04 09:52:59