Optimalizace vyhledávání leptoquarků pomocí strojového učení v datech z CERN ATLAS experiment
Optimization of Machine Learning for the Leptoquark Search Using CERN ATLAS Data
Typ dokumentu
bakalářská prácebachelor thesis
Autor
Janick Böhm
Vedoucí práce
Sopczak André
Oponent práce
Petousis Vlasios
Studijní program
Electrical Engineering and Computer ScienceInstituce přidělující hodnost
katedra kybernetikyPráva
A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.htmlVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html
Metadata
Zobrazit celý záznamAbstrakt
The Leptoquark is among the undiscovered particles which are being searched for in the Large Hadron Collider. Monte Carlo simulated events of proton-to-proton collisions corresponding to the Leptoquark are studied with the ATLAS detector. The luminosity of the produced samples corresponds to the recorded data of 140 fb^-1. Four machine learning algorithms are used (TabNet, XGBoost, MLP, and Bayesian MLP) to train models to separate events on the 2lSS + 1tau channel belonging to the pair-production mode of Leptoquark from various background processes, including ttH, ttW, ttZ, tt, VV and other minor processes. The feature importance of the top performing models is constructed and utilized to produce more efficient models with improved sensitivity. In addition, the expected upper limit of cross-section for the pair-production of Leptoquark at 95% confidence level is calculated and compared to existing results. The Leptoquark is among the undiscovered particles which are being searched for in the Large Hadron Collider. Monte Carlo simulated events of proton-to-proton collisions corresponding to the Leptoquark are studied with the ATLAS detector. The luminosity of the produced samples corresponds to the recorded data of 140 fb^-1. Four machine learning algorithms are used (TabNet, XGBoost, MLP, and Bayesian MLP) to train models to separate events on the 2lSS + 1tau channel belonging to the pair-production mode of Leptoquark from various background processes, including ttH, ttW, ttZ, tt, VV and other minor processes. The feature importance of the top performing models is constructed and utilized to produce more efficient models with improved sensitivity. In addition, the expected upper limit of cross-section for the pair-production of Leptoquark at 95% confidence level is calculated and compared to existing results.
Kolekce
- Bakalářské práce - 13133 [714]