Particle Cloud Generation with Message Passing Generative Adversarial Networks

Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago R F P Tomei, Maurizio Pierini, Mary Touranakou, Jean-roch Vlimant, Dimitrios Gunopulos

Keywords: generative models, gans, point clouds, physics

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 Fréchet 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.

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TL;DR: We publish a new point-cloud-based high energy physics dataset, and use it to develop a message-passing GAN (MPGAN) approach to simulate particle collisions.

Code: https://github.com/rkansal47/MPGAN/tree/neurips21