Machine Learning in the Search for New Fundamental Physics

• URL: http://arxiv.org/abs/2112.03769v1
• Date: Tue, 7 Dec 2021 15:26:42 GMT
• Title: Machine Learning in the Search for New Fundamental Physics
• Authors: Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, and David Shih
• Abstract summary: Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments.
• Score: 0.32622301272834514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy physics experiments, including the Large Hadron Collider, rare event searches, and neutrino experiments. While machine learning has a long history in these fields, the deep learning revolution (early 2010s) has yielded a qualitative shift in terms of the scope and ambition of research. These modern machine learning developments are the focus of the present review.

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