Publication Date
2023
Document Type
Dissertation/Thesis
First Advisor
Eads, Michael
Degree Name
M.S. (Master of Science)
Legacy Department
Department of Physics
Abstract
This work reports on the use of different machine learning (ML) techniques in the search for vector boson scattering (VBS) events in the semileptonic $WV$ channel. VBS is an important process for studying electroweak symmetry breaking (EWSB), the Higgs mechanism, as well as for probing beyond the standard model physics. Boosted decision trees as well as deep neural networks were trained on Monte Carlo simulation samples and applied to 137 fb$^{-1}$ of proton-proton collision data taken from 2016 to 2018 by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) with a center of mass energy $\sqrt{s} = 13$ TeV. The ML model hyperparameters and inputs were varied to find the best performing combination, and the results of those models are discussed.
Recommended Citation
Mekosh, Mark, "Using Machine Learning to Search for Vector Boson Scattering at the CMS Detector During Run 2" (2023). Graduate Research Theses & Dissertations. 7164.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7164
Extent
109 pages
Language
eng
Publisher
Northern Illinois University
Rights Statement
In Copyright
Rights Statement 2
NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.
Media Type
Text
