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 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 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.

本文介绍了高能物理领域的 Jets 和它们在 LHC 对撞机中的存在，针对该领域存在的数据表示问题，提出了一种新的基于 Particle cloud 的数据集 JetNet，并开发出名为 MPGAN 的新型生成模型，实验结果表明 MPGAN 在各项指标上均表现优异，未来有望被用于高能物理领域。同时，为了促进研究和提高可访问性和可重复性，开源了 JetNet Python Package，提供了接口，实现了评估和损失度量等工具。

利用消息传递生成对抗网络生成粒子云

Particle Cloud Generation with Message Passing Generative Adversarial  Networks

How to represent a jet is at the core of machine learning on jet physics.
Inspired by the notion of point clouds, we propose a new approach that
considers a jet as an unordered set of its constituent particles, effectively a
"particle cloud". Such a particle cloud representation of jets is efficient in
incorporating raw information of jets and also explicitly respects the
permutation symmetry. Based on the particle cloud representation, we propose
ParticleNet, a customized neural network architecture using Dynamic Graph
Convolutional Neural Network for jet tagging problems. The ParticleNet
architecture achieves state-of-the-art performance on two representative jet
tagging benchmarks and is improved significantly over existing methods.

本文提出了一种有效地将喷流表示为其组成粒子的无序集合的新方法 “ParticleNet”，该方法可以显式地支持置换对称性，使用这种方法，在两个代表性的喷流标记基准测试中，ParticleNet 的性能达到了最先进水平并显著优于现有方法。