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.
Highlights
Particle physics focuses on understanding fundamental laws of nature by observing elementary particles, either in controlled environments or in nature
Inspired by the success deep learning has achieved at reaching super-human performance at various tasks, various domains in the physical sciences [2], including particle physics [1, 3,4,5,6], have begun exploring deep learning as a unique tool for handling difficult scientific problems that go beyond straightforward classification, to organize and make sense of vast data sources, draw inferences about unobserved causal factors, and even discover physical principles underpinning complex phenomena [7, 8]
High Energy Physics (HEP) experiments often use machine learning for learning complicated inverse functions, trying to infer something about the underlying physics process from the information measured in the detector
Summary
Original Content from Abstract this work may be used Particle physics is a branch of science aiming at discovering the fundamental laws of matter and under the terms of the Creative Commons forces. Graph neural networks are trainable functions which operate on graphs—sets of elements. Any further distribution of this work must learning. They are very expressive and have demonstrated superior performance to other classical maintain attribution to the author(s) and the title deep learning approaches in a variety of domains. The data in particle physics are often represented of the work, journal by sets and graphs and as such, graph neural networks offer key advantages. 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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