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

The success of convolutional networks in learning problems involving planar
signals such as images is due to their ability to exploit the translation
symmetry of the data distribution through weight sharing. Many areas of science
and egineering deal with signals with other symmetries, such as rotation
invariant data on the sphere. Examples include climate and weather science,
astrophysics, and chemistry. In this paper we present spherical convolutional
networks. These networks use convolutions on the sphere and rotation group,
which results in rotational weight sharing and rotation equivariance. Using a
synthetic spherical MNIST dataset, we show that spherical convolutional
networks are very effective at dealing with rotationally invariant
classification problems.

本文介绍了一种新的网络结构 —— 球面卷积网络，用于处理具有旋转不变性的分类问题，并通过在球面上进行卷积和旋转权重共享来实现旋转等变性。实验结果表明，球面卷积网络在处理类似于 MNIST 的数据集时具有很高的准确率。