EPiC-GAN: Equivariant point cloud generation for particle jets

Journal Article · 03 October 2023 · SciPost Physics

DOI: https://doi.org/10.21468/SciPostPhys.15.4.130 · OSTI ID:2007428

Buhmann, Erik ^[1]; Kasieczka, Gregor ^[2]; Thaler, Jesse ^[3]

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1. University of Hamburg
2. Center for Data and Computing in Natural Sciences, University of Hamburg
3. Massachusetts Institute of Technology, The NSF AI Institute for Artificial Intelligence and Fundamental Interactions

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.

• Sponsoring Organization: USDOE
• Grant/Contract Number: SC0012567
• OSTI ID: 2007428
• Journal Information: SciPost Physics, Journal Name: SciPost Physics Journal Issue: 4 Vol. 15; ISSN 2542-4653
• Publisher: Stichting SciPostCopyright Statement
• Country of Publication: Netherlands
• Language: English