Graph Neural Networks in Particle Physics

TL;DR

This paper reviews how graph neural networks are applied in particle physics, highlighting their advantages, various architectures, and open problems where they can be particularly effective.

Contribution

It provides a comprehensive overview of the applications, architectures, and challenges of graph neural networks in the context of particle physics research.

Findings

Graph neural networks outperform classical methods in particle physics tasks.

Various graph constructions and architectures are explored for different applications.

Open problems in particle physics can benefit from graph neural network approaches.

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.