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Title:A search for supersymmetry with the ATLAS detector, and the use of machine learning techniques for object classification in high energy physics
Author(s):Zhang, Matt
Director of Research:Hooberman, Ben
Doctoral Committee Chair(s):Neubauer, Mark
Doctoral Committee Member(s):Cooper, Lance; Shelton, Jessie
Department / Program:Physics
Discipline:Physics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):particle physics
high energy physics
HEP
machine learning
ATLAS
CERN
data science
supersymmetry
Abstract:We conduct a search for supersymmetry using data from the ATLAS detector at CERN, in a region with 2 leptons, 2 jets, and large MET. We also demonstrate the development of various machine learning techniques to enhance similar physics searches in the future, including the use of neural nets on calorimeter data for particle-type classification, particle energy regression, and shower generation.
Issue Date:2021-01-12
Type:Thesis
URI:http://hdl.handle.net/2142/110404
Rights Information:Copyright 2021 Matt Zhang
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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