Publication Date

5-1-2020

Document Type

Dissertation/Thesis

First Advisor

Adelman, Jahred

Degree Name

B.S. (Bachelor of Science)

Department

Department of Physics

Abstract

Accurately analyzing the monumental amount of data sourced from high-energy particle experiments presents a herculean task. Some methods under investigation for event analysis, particularly while searching for low-probability events, are machine learning algorithms. Tyler Burch has developed a Boosted Decision Tree (BDT) to look for Vector Boson Fusion (VBF) events through di-Higgs production. VBF is a di-Higgs production process. This report investigates the performance of the BDT if given simulated collision data produced by varying the interaction constants in VBF hhjj production away from those predicted by the Standard Model. The test range will focus on 3 coupling constants—λ, cvv, and cv, governing HHH, VVHH, and VVH vertexes respectively—varying from 0 to 3 normalized to the standard model for c2v and cv and 0 to 11 for λ. This is an analysis for the ATLAS experiment at the LHC.

Extent

21 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

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