Interdisciplinary Machine Learning Methods for Particle Physics: The Search for the Higgs Boson Produced in Association with a Leptonically-Decaying Vector Boson and Decaying to a Tau Pair, Hadronic Tau Identification in the ATLAS High-Level Trigger, and Predictions of Many-Body System Dynamics

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 $\mu = \sigma/\sigma_{SM} < 5.6$ with 95\% confidence using 20.3 fb$^{-1}$ of collision data collected at a center-of-mass energy of $\sqrt{s}=8$ 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 $\sqrt{s}=13$ 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.