Particle Cloud Generation with Message Passing Generative Adversarial Networks

TL;DR

This paper introduces JetNet, a new particle cloud dataset for high energy physics, and develops MPGAN, a message passing GAN that outperforms existing models in generating particle clouds, aiding LHC jet simulations.

Contribution

The paper presents JetNet as a novel dataset and proposes MPGAN, a message passing GAN tailored for particle cloud generation in high energy physics, establishing new benchmarks.

Findings

MPGAN outperforms existing point cloud GANs on multiple metrics.

JetNet provides a new benchmark dataset for ML in high energy physics.

Open-source tools facilitate research and reproducibility in particle cloud generation.

Abstract

In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machine learning (ML)-based generative models, such as generative adversarial networks (GANs), have the potential to significantly accelerate LHC jet simulations. However, despite jets having a natural representation as a set of particles in momentum-space, a.k.a. a particle cloud, there exist no generative models applied to such a dataset. In this work, we introduce a new particle cloud dataset (JetNet), and apply to it existing point cloud GANs. Results are evaluated using (1) 1-Wasserstein distances between high- and low-level feature distributions, (2) a newly developed Fr\'{e}chet ParticleNet Distance, and (3) the coverage and (4) minimum matching distance metrics. Existing GANs are found to be inadequate for physics applications, hence we develop a new message passing GAN (MPGAN), which outperforms existing point cloud GANs on virtually every metric and shows promise for use in HEP. We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models. Additionally, to facilitate research and improve accessibility and reproducibility in this area, we release the open-source JetNet Python package with interfaces for particle cloud datasets, implementations for evaluation and loss metrics, and more tools for ML in HEP development.