EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion

• URL: http://arxiv.org/abs/2310.00049v1
• Date: Fri, 29 Sep 2023 18:00:03 GMT
• Title: EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
• Authors: Erik Buhmann, Cedric Ewen, Darius A. Faroughy, Tobias Golling, Gregor Kasieczka, Matthew Leigh, Guillaume Qu\'etant, John Andrew Raine, Debajyoti Sengupta, David Shih
• Abstract summary: We present two novel methods that generate LHC jets as point clouds efficiently and accurately. epcjedi and ep both achieve state-of-the-art performance on the top-quark JetNet datasets.
• Score: 0.7255608805275865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two novel methods that generate LHC jets as point clouds efficiently and accurately. We introduce \epcjedi, which combines score-matching diffusion models with the Equivariant Point Cloud (EPiC) architecture based on the deep sets framework. This model offers a much faster alternative to previous transformer-based diffusion models without reducing the quality of the generated jets. In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation. This model is trained with {\it flow-matching}, a scalable and easy-to-train objective based on optimal transport that directly regresses the vector fields connecting the Gaussian noise prior to the data distribution. Our experiments demonstrate that \epcjedi and \epcfm both achieve state-of-the-art performance on the top-quark JetNet datasets whilst maintaining fast generation speed. Most notably, we find that the \epcfm model consistently outperforms all the other generative models considered here across every metric. Finally, we also introduce two new particle cloud performance metrics: the first based on the Kullback-Leibler divergence between feature distributions, the second is the negative log-posterior of a multi-model ParticleNet classifier.

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