Deep learning methods have been increasingly adopted to study jets in
particle physics. Since symmetry-preserving behavior has been shown to be an
important factor for improving the performance of deep learning in many
applications, Lorentz group equivariance - a fundamental spacetime symmetry for
elementary particles - has recently been incorporated into a deep learning
model for jet tagging. However, the design is computationally costly due to the
analytic construction of high-order tensors. In this article, we introduce
LorentzNet, a new symmetry-preserving deep learning model for jet tagging. The
message passing of LorentzNet relies on an efficient Minkowski dot product
attention. Experiments on two representative jet tagging benchmarks show that
LorentzNet achieves the best tagging performance and improves significantly
over existing state-of-the-art algorithms. The preservation of Lorentz symmetry
also greatly improves the efficiency and generalization power of the model,
allowing LorentzNet to reach highly competitive performance when trained on
only a few thousand jets. Code and models are available at
https://github.com/sdogsq/LorentzNet-release.

本论文提出了一种新的深度学习模型，称为 LorentzNet，用于粒子物理中的 Jet 标注问题，该模型维持了洛伦兹对称性，通过高效的闵可夫斯基点积注意力进行信息传递，在两个代表性的 Jet 标注引擎上进行的实验表明，LorentzNet 表现出最佳性能并显著改善了现有的最先进算法。