Searching for DFSZ Axinos at the ATLAS Experiment, and Developing Novel Machine Learning Methods for High-Energy Physics

This thesis presents work on two distinct but thematically-related topics in high-energy physics. The first is a search for displaced vertices and missing transverse energy, conducted using 137 inverse femtobarns of 13 TeV data collected by the ATLAS detector during Run 2, that targets beyond-Standard Model processes that produce long-lived particles. I will explain the analysis method and present its results, giving particular attention to its relevance to and interpretation in the context of a model combining axion physics with supersymmetry.

The second topic -- addressing  the complex reconstruction tasks inherent to collider physics analyses -- is a Lorentz-equivariant neural network architecture called PELICAN. I will describe the basic network design, and results from top quark identification and momentum reconstruction benchmarks.