Metrics, Muons, Moments, Models, Machine Learning, Measurements, and More: A Manifesto on Collider Physics

Abstract

The interface between particle theory and particle experiments is essential to improving our understanding of the Standard Model and looking for new physics beyond it. At this interface lies a complicated web of complex and expensive simulations that cannot fully be trusted, experimental and theoretical uncertainties, overwhelmingly large amounts of data, all while we have yet to find any deviations from the Standard Model.

In this thesis, we propose strategies for improving the theory ↔ experiment pipeline at all stages. We first show how modern Machine Learning and statistical techniques can be used to improve the calibration and resolution of particle detectors in a robust way, which can lead to improved measurement precision. We then develop brand new classes of measurable observables based on the principle of infrared-and-collinear-safety, geometry, and machine learning, which come with guarantees about their theoretical calculability and interpretability, in turn motivating measurements at collider experiments. Finally, we then present two complementary approaches to search for new physics: one, in the form of an experimental proposal for a muon beam dump experiment that is viable alongside a full future collider program; and the other, in the form of machine-learning based anomaly detection to search for subtle signals in already-published data.