Date of Award
Spring 2021
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Physics
First Advisor
Demers, Sarah
Abstract
Following the discovery of a Higgs boson-like particle in the summer of 2012 at the Large Hadron Collider (LHC) at CERN, the high-energy particle physics community has prioritized its thorough study. As part of a comprehensive plan to investigate the many combinations of production and decay of the Standard Model Higgs boson, this thesis describes a continued search for this particle produced in association with a leptonically-decaying vector boson (i.e. a W or Z boson) and decaying into a pair of tau leptons. In Run 1 at the LHC, ATLAS researchers were able to set an upper constraint on the signal strength of this process at μ = σ/σ_SM < 5.6 with 95% confidence using 20.3 fb^-1 of collision data collected at a center-of-mass energy of √s = 8 TeV. My thesis work, which builds upon and extends the Run 1 analysis structure, takes advantage of an increased center-of-mass energy in Run 2 of the LHC of √s = 13 TeV as well as 139 fb^-1 of data, approximately seven times the amount used for the Run 1 analysis. While the higher center-of-mass energy in Run 2 yields a higher expected cross-section for this process, the analysis faces the additional challenges of two newly-considered final states, a higher number of simultaneous interactions per event, and a novel neural network-based background estimation technique. I also describe advanced machine learning techniques I have developed to support tau identification in the ATLAS High-Level Trigger as well as predicting and analyzing the dynamics of many-body systems such as 3D motion capture data of choreography.
Recommended Citation
Pettee, Mariel, "Interdisciplinary Machine Learning Methods for Particle Physics" (2021). Yale Graduate School of Arts and Sciences Dissertations. 160.
https://elischolar.library.yale.edu/gsas_dissertations/160