Machine Learning in the Search for New Fundamental Physics
High Energy Physics - Phenomenology
2021-12-08 v1 High Energy Physics - Experiment
Data Analysis, Statistics and Probability
Machine Learning
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.
Keywords
Cite
@article{arxiv.2112.03769,
title = {Machine Learning in the Search for New Fundamental Physics},
author = {Georgia Karagiorgi and Gregor Kasieczka and Scott Kravitz and Benjamin Nachman and David Shih},
journal= {arXiv preprint arXiv:2112.03769},
year = {2021}
}
Comments
Preprint of article submitted to Nature Reviews Physics, 19 pages, 1 figure