B.S. (Bachelor of Science)
Department of Physics
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.
Coveyou, Dayne R., "Studying the Flexibility of a BDT as a VBF di-Higgs Production Analysis Tool" (2020). Honors Capstones. 1061.
Northern Illinois University
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