Year: 2026
Type: article
Abstract: High-energy experimental facilities such as the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) are collecting more data and making more complex measurements than ever before. Machine learning has proven to be a valuable tool for these efforts that can be used throughout the pipeline from data collection to analysis. Such techniques will become necessary at future facilities such as the Electron Ion Collider (EIC) and the High Luminosity LHC (HL-LHC). These proceedings summarize a selection of recent developments on the use of machine learning as a technique for physics analysis and provide an outlook for future use.
Author: Hannah Bossi
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7154973589
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