Graph neural networks in particle physics

• Published in: Machine learning: science and technology Vol. 2; no. 2; pp. 21001 - 21018
• Main Authors: Shlomi, Jonathan, Battaglia, Peter, Vlimant, Jean-Roch
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.06.2021
• Subjects: Deep learning; graph neural network; Graph neural networks; Graphical representations; Graphs; high energy physics; machine learning; Neural networks; Particle physics; review
• ISSN: 2632-2153; 2632-2153
• DOI: 10.1088/2632-2153/abbf9a

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.