Publication Date


Document Type


First Advisor

Eads, Michael

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Physics


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.


109 pages




Northern Illinois University

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In Copyright

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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.

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Physics Commons