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

We explore the metric space of jets using public collider data from the CMS
experiment. Starting from 2.3/fb of 7 TeV proton-proton collisions collected at
the Large Hadron Collider in 2011, we isolate a sample of 1,690,984 central
jets with transverse momentum above 375 GeV. To validate the performance of the
CMS detector in reconstructing the energy flow of jets, we compare the CMS Open
Data to corresponding simulated data samples for a variety of jet kinematic and
substructure observables. Even without detector unfolding, we find very good
agreement for track-based observables after using charged hadron subtraction to
mitigate the impact of pileup. We perform a range of novel analyses, using the
"energy mover's distance" (EMD) to measure the pairwise difference between jet
energy flows. The EMD allows us to quantify the impact of detector effects,
visualize the metric space of jets, extract correlation dimensions, and
identify the most and least typical jet configurations. To facilitate future
jet studies with CMS Open Data, we make our datasets and analysis code
available, amounting to around two gigabytes of distilled data and one hundred
gigabytes of simulation files.

本文使用 CMS 探测器公开的对撞机数据探索喷注的度量空间，使用 “能流移动距离”（EMD）来衡量喷注能流的成对差异，量化探测器效应，提取相关维度，并确定最典型和最不典型的喷注配置。