UC Irvine

This work explores the application of machine learning for multi-prong jet classification at the LHC. We compare the performance of high-level networks trained on jet observables to low-level networks trained on the full event information. Our focus is on an extreme scenario, evaluating the performance of the classifiers on jets with a large number of sub-jets, larger than previously tested in the literature. The results indicate that traditional jet observables like $N$-subjettiness and Energy-Flow Polynomials are good discriminants when tested on jets with up to eight hard sub-jets, but that there is information in the events that is untapped by these observables. We introduce Jet Rotational Metrics, a new family of observables designed to capture features of the degree of discrete rotational symmetry of jets. These observables prove highly valuable for the classification task, bridging the gap between the low- and high-level networks.

This dissertation also introduces a technique for the accurate estimation of systematic uncertainties in physics studies using Gaussian process regression. A typical approach is to assume the factorization of the various sources of systematic uncertainties. This approach is often extended to assume that the impact of these individual sources of uncertainty on observables of the detector response also factories. Our technique uses Gaussian processes to model observables as functions of the nuisance parameters. We show that this technique is more accurate than the factorized approach and that it can learn from limited samples by including gradient information. Additionally, we present a Bayesian-based sampling strategy that efficiently explores the space of experimental response while reducing the predictive uncertainty of the Gaussian process.