Graph Neural Networks for Particle Tracking and Reconstruction

TL;DR

This paper reviews the application of graph neural networks (GNNs) in high energy physics for particle tracking and reconstruction, emphasizing their advantages over traditional image-based methods and discussing design considerations.

Contribution

It provides a comprehensive overview of GNN formalism, design considerations, and promising applications in particle physics data analysis.

Findings

GNNs naturally model particle detector data as graphs.

Design choices significantly impact GNN performance in HEP.

GNNs show promise for future particle tracking tasks.

Abstract

Machine learning methods have a long history of applications in high energy physics (HEP). Recently, there is a growing interest in exploiting these methods to reconstruct particle signatures from raw detector data. In order to benefit from modern deep learning algorithms that were initially designed for computer vision or natural language processing tasks, it is common practice to transform HEP data into images or sequences. Conversely, graph neural networks (GNNs), which operate on graph data composed of elements with a set of features and their pairwise connections, provide an alternative way of incorporating weight sharing, local connectivity, and specialized domain knowledge. Particle physics data, such as the hits in a tracking detector, can generally be represented as graphs, making the use of GNNs natural. In this chapter, we recapitulate the mathematical formalism of GNNs and highlight aspects to consider when designing these networks for HEP data, including graph construction, model architectures, learning objectives, and graph pooling. We also review promising applications of GNNs for particle tracking and reconstruction in HEP and summarize the outlook for their deployment in current and future experiments.