{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/142702"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/142702","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Machine Learning for High-Energy Collider Physics","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Komiske III, Patrick Theodore"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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