Machine Learning for High-Energy Collider Physics
Name
komiske-pkomiske-phd-physics-2021-thesis.pdf
Description
Thesis PDF
Size
16.48 MB
Format
Adobe PDF
Checksum (MD5)
b6f12131d3d49495b096e907ef89edac
Author(s)
Komiske III, Patrick Theodore
Advisor(s)
Thaler, Jesse
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
Abstract
Fundamental physics, in particular high-energy collider physics, seeks to understand the natural world at the smallest scales, leading experimentally to the creation of large, complex datasets. Machine learning comprises a powerful set of statistical and computational tools enabling comprehensive exploitation of data. In this thesis, I develop machine learning methods to facilitate cutting-edge analysis techniques in particle physics. I model collider events as point clouds and develop neural network architectures that respect the inherent permutation symmetry and variable number of particles of an event, with infrared safety naturally incorporated. I further design a procedure that uses high-dimensional classifiers to achieve full-phase space, unbinned unfolding of all observables simultaneously. In the second part of this thesis, I define a distance metric between collider events based on optimal transport that allows for a rigorous construction of "event space" and its corresponding geometry. Using public datasets provided by the CMS collaboration, I explore this metric on a dataset of real jets, demonstrating its viability as an experimental method as well as the value of public collider data in benchmarking new techniques.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
