Graph Neural Networks in Particle Physics

Abstract

Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs—sets of elements and their pairwise relations—and are a central method within the broader field of geometric deep learning. They are very expressive and have demonstrated superior performance to other classical deep learning approaches in a variety of domains. The data in particle physics are often represented by sets and graphs and as such, graph neural networks offer key advantages. Here we review various applications of graph neural networks in particle physics, including different graph constructions, model architectures and learning objectives, as well as key open problems in particle physics for which graph neural networks are promising.

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© 2020 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 27 July 2020; Accepted 8 October 2020; Published 29 December 2020. We thank Thomas Keck for valuable feedback on the manuscript. J S is supported by the NSF-BSF Grant 2017600 and the ISF Grant 125756 and partially supported by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center. J-R V is partially supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant No. 772369) and by the U S Department of Energy, Office of Science, Office of High Energy Physics under Grant Nos. DE-SC0011925, DE-SC0019227 and DE-AC02-07CH11359. Data sharing is not applicable to this article as no new data were created or analysed in this study.

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Published - Shlomi_2021_Mach._Learn.__Sci._Technol._2_021001.pdf

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