Leveraging Machine Learning in the Search for New Bosons at the LHC and Other Resulting Applications

Abstract

This dissertation focuses on the use of semi-supervised machine learning for data generation in high-energy physics, specifically to aid in the search for new bosons at the Large Hadron Collider. The overarching physics analysis for this work involves the development of a generative machine learning model to assist in the search for resonances in the Zγ final state background data. A number of Variational Auto-encoder (VAE) derivatives are developed and trained to be able to generate a chosen Monte Carlo fast simulated dataset. These VAE derivatives are then evaluated using chosen metrics and plots to assess their performance in data generation. Overall, this work aims to demonstrate the utility of semi-supervised machine learning techniques in the search for new resonances in high-energy physics. Additionally, a resulting application of the use of machine learning in COVID-19 crisis management was also documented.