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

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Physics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Zhang, Matt

Contributors dc:contributor

• Hooberman, Ben
• Neubauer, Mark
• Cooper, Lance
• Shelton, Jessie

• particle physics
• high energy physics
• HEP
• machine learning
• ATLAS
• CERN
• data science
• supersymmetry

Statement dc:rights

• Copyright 2021 Matt Zhang

Language dc:language

en

Handle dc:identifier

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

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110404