We introduce the Particle Convolution Network (PCN), a new type of
equivariant neural network layer suitable for many tasks in jet physics. The
particle convolution layer can be viewed as an extension of Deep Sets and
Energy Flow network architectures, in which the permutation-invariant operator
is promoted to a group convolution. While the PCN can be implemented for
various kinds of symmetries, we consider the specific case of rotation about
the jet axis the $\eta - \phi$ plane. In two standard benchmark tasks, q/g
tagging and top tagging, we show that the rotational PCN (rPCN) achieves
performance comparable to graph networks such as ParticleNet. Moreover, we show
that it is possible to implement an IRC-safe rPCN, which significantly
outperforms existing IRC-safe tagging methods on both tasks. We speculate that
by generalizing the PCN to include additional convolutional symmetries relevant
to jet physics, it may outperform the current state-of-the-art set by graph
networks, while offering a new degree of control over physically-motivated
inductive biases.

介绍了一种新型的粒子卷积网络层 (Particle Convolution Network, PCN)，适用于喷注物理学中的许多任务。在两个标准基准任务中，表明可将 PCN 推广以包括与喷注物理学相关的其他卷积对称性，从而超越图网络设置，同时提供物理驱动的归纳偏差控制的新程度

高能物理中的粒子卷积

Particle Convolution for High Energy Physics

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。

ParticleNet：基于粒子云的喷注标记

ParticleNet: Jet Tagging via Particle Clouds

Recent progress in applying machine learning for jet physics has been built
upon an analogy between calorimeters and images. In this work, we present a
novel class of recursive neural networks built instead upon an analogy between
QCD and natural languages. In the analogy, four-momenta are like words and the
clustering history of sequential recombination jet algorithms is like the
parsing of a sentence. Our approach works directly with the four-momenta of a
variable-length set of particles, and the jet-based tree structure varies on an
event-by-event basis. Our experiments highlight the flexibility of our method
for building task-specific jet embeddings and show that recursive architectures
are significantly more accurate and data efficient than previous image-based
networks. We extend the analogy from individual jets (sentences) to full events
(paragraphs), and show for the first time an event-level classifier operating
on all the stable particles produced in an LHC event.

本文介绍了一种基于量子色动力学和自然语言的类递归神经网络，该方法在从可变长度集合的粒子四动量中建立任务特定的喷注嵌入方面具有灵活性，在所有稳定粒子上操作的事件级分类器首次展示了类比从单个喷注（句子）扩展至完整事件（段落）的能力。