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Poster
Particle Cloud Generation with Message Passing Generative Adversarial Networks
Raghav Kansal · Javier Duarte · Hao Su · Breno Orzari · Thiago Tomei · Maurizio Pierini · Mary Touranakou · jean-roch vlimant · Dimitrios Gunopulos

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @
in Poster Session 2 »

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.

Keywords: Machine Learning · Generative Model

Author Information

Raghav Kansal (UC San Diego)
Javier Duarte (University of California San Diego)
Hao Su (Stanford)
Breno Orzari (Instituto de Física Teórica - UNESP)
Thiago Tomei (Universidade Estadual Paulista)
Maurizio Pierini (CERN)
Mary Touranakou (CERN)
jean-roch vlimant (California Institute of Technology)
Dimitrios Gunopulos

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