Particle Cloud Generation with Message Passing Generative Adversarial Networks

• URL: http://arxiv.org/abs/2106.11535v1
• Date: Tue, 22 Jun 2021 04:21:16 GMT
• Title: Particle Cloud Generation with Message Passing Generative Adversarial Networks
• Authors: Raghav Kansal, Javier Duarte, Hao Su, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopulos
• Abstract summary: In high energy physics, jets are collections of correlated particles produced ubiquitously in particle collisions. Machine-learning-based generative models, such as generative adversarial networks (GANs), have the potential to significantly accelerate LHC jet simulations. We introduce a new particle cloud dataset (JetNet), and, due to similarities between particle and point clouds, apply to it existing point cloud GANs.
• Score: 14.737885252814273
• License: http://creativecommons.org/licenses/by/4.0/
• 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-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, to our knowledge there exist no generative models applied to such a dataset. We introduce a new particle cloud dataset (JetNet), and, due to similarities between particle and point clouds, apply to it existing point cloud GANs. Results are evaluated using (1) the 1-Wasserstein distance 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 machine learning community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models.

Related papers

• DeepTreeGANv2: Iterative Pooling of Point Clouds [0.0]
  We present an extension to DeepTreeGAN, featuring a critic, that is able to aggregate point clouds iteratively in a tree-based manner. We show that this model can reproduce complex distributions, and we evaluate its performance on the public JetNet 150 dataset.
  arXiv  Detail & Related papers  (2023-11-24T10:42:11Z)
• EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion [0.7255608805275865]
  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.
  arXiv  Detail & Related papers  (2023-09-29T18:00:03Z)
• Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach [83.05340155068721]
  We devise a new 3d point cloud generation framework using a divide-and-conquer approach. All patch generators are based on learnable priors, which aim to capture the information of geometry primitives. Experimental results on a variety of object categories from the most popular point cloud dataset, ShapeNet, show the effectiveness of the proposed patch-wise point cloud generation.
  arXiv  Detail & Related papers  (2023-07-22T11:10:39Z)
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation [0.0]
  Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. This work achieves a major breakthrough by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure.
  arXiv  Detail & Related papers  (2023-05-08T16:44:15Z)
• StarNet: Style-Aware 3D Point Cloud Generation [82.30389817015877]
  StarNet is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network. Our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks.
  arXiv  Detail & Related papers  (2023-03-28T08:21:44Z)
• Controllable Mesh Generation Through Sparse Latent Point Diffusion Models [105.83595545314334]
  We design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. Our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability.
  arXiv  Detail & Related papers  (2023-03-14T14:25:29Z)
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets [0.0]
  We introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
  arXiv  Detail & Related papers  (2023-01-17T19:00:00Z)
• Transformer with Implicit Edges for Particle-based Physics Simulation [135.77656965678196]
  Transformer with Implicit Edges (TIE) captures the rich semantics of particle interactions in an edge-free manner. We evaluate our model on diverse domains of varying complexity and materials.
  arXiv  Detail & Related papers  (2022-07-22T03:45:29Z)
• Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction [98.38585659305325]
  normalizing flows (NFs) have demonstrated state-of-the-art performance on modeling 3D point clouds. This work enhances their representational power by applying mixtures of NFs to point clouds.
  arXiv  Detail & Related papers  (2021-06-06T14:25:45Z)
• Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics [1.6417409087671928]
  We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC) We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC.
  arXiv  Detail & Related papers  (2020-11-30T23:53:45Z)
• Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification [136.57669231704858]
  We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model. We call our model the Generative PointNet because it can be derived from the discriminative PointNet.
  arXiv  Detail & Related papers  (2020-04-02T23:08:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.