Machine Learning for High-Energy Collider Physics
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 ex...
Main Author: | Komiske III, Patrick Theodore |
---|---|
Other Authors: | Thaler, Jesse |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2022
|
Online Access: | https://hdl.handle.net/1721.1/142702 |
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