University of Illinois Urbana-Champaign

A search for supersymmetry with the ATLAS detector, and the use of machine learning techniques for object classification in high energy physics

Zhang, Matt

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https://hdl.handle.net/2142/110404

Description

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
Issue Date
2021-01-12
Director of Research (if dissertation) or Advisor (if thesis)
Hooberman, Ben
Doctoral Committee Chair(s)
Neubauer, Mark
Committee Member(s)
  • Cooper, Lance
  • Shelton, Jessie
Department of Study
Physics
Discipline
Physics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(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.
Graduation Semester
2021-05
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/110404
Copyright and License Information
Copyright 2021 Matt Zhang

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