Graph Neural Networks in Particle Physics (2007.13681v2)

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.

• The paper reviews the application of GNNs in particle physics, demonstrating enhanced classification in jets, nodes, and events.
• It details innovative methodologies like dynamic graph connectivity and edge convolutions to improve model performance.
• The review outlines future research directions, including optimal GNN architectures and generative modeling for high energy physics.

Graph Neural Networks in Particle Physics

The paper "Graph Neural Networks in Particle Physics" by Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant provides a comprehensive review of the applications of Graph Neural Networks (GNNs) within the domain of particle physics. The exploration of GNNs is framed within the context of addressing complex problems that arise from the data typically represented as sets or graphs within high energy physics (HEP).

Introduction

Particle physics is concerned with elucidating the fundamental laws governing matter and forces, involving the observation of elementary particles through experiments either conducted in collider physics or naturally occurring phenomena. Traditional machine learning methods like boosting decision trees or support vector machines have been pivotal in this domain, but the advent of deep learning and, more specifically, GNNs has introduced transformative potential due to their superior expressivity and performance across various domains.

Geometric Deep Learning Overview

The review situates GNNs within the broader family of geometric deep learning, extending deep learning beyond conventional data structures like vectors, grids, or sequences. GNNs embody strong relational inductive biases, essential for learning functions operating over graphs defined as sets of nodes and edges. This is particularly relevant as particle physics data inherently possess rich structural relationships better modeled by graphs.

Applications in Particle Physics

Graph Classification:

Applications in jet classification are highlighted, where GNNs have demonstrated improved performance over traditional architectures for distinguishing jets from different processes. For instance, models like ParticleNet adapt dynamic graph connectivity using edge convolution techniques to classify jets, showing enhanced accuracy in benchmarks.

Node and Edge Classification:

GNNs are also deployed in node classification tasks such as pileup mitigation, where they outperform classical methods by effectively differentiating primary from secondary interactions in the particle detector's environment. In edge classification, these methodologies have shown promise in reconstructing charged particle trajectories by identifying consistent hit sequences in tracking detectors.

Event Classification:

The review discusses the use of GNNs for event-level predictions, such as classifying neutrino events in the IceCube detector, leveraging graph convolution methods to surpass existing analysis efficiencies.

Model Architecture and Considerations

The paper underscores various architectural choices and the critical aspect of graph construction, which impacts computational efficiency and model performance. For instance, the considerations include dynamic edge creation, attention mechanisms to prioritize crucial particles, and stacking GN blocks to enhance expressivity. The review also points to emerging architectures like Transformers and Reformers, which afford computational efficiency through sparse connections and low-rank approximations.

Implications and Future Directions

Numerous open questions remain regarding the optimal architecture for GNNs in various particle physics tasks and the extent to which physical inductive biases can be effectively integrated. The paper also points to potential avenues in generative modeling, where GNNs could revolutionize data simulation tasks, a critical element given the resource intensity of traditional simulation methods in HEP.

In conclusion, the review articulates a forward-looking perspective by emphasizing the promise of GNNs in advancing both theoretical understanding and practical applications in particle physics. As research continues to mature, it can be anticipated that these methodologies will play a pivotal role in uncovering new physical insights and enhancing the precision of analyses in experimental physics contexts.