An efficient Lorentz equivariant graph neural network for jet tagging
An efficient Lorentz equivariant graph neural network for jet tagging
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DOI：
10.1007/jhep07(2022)030
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发表时间：
2022-01
影响因子：
5.4
通讯作者：
Shiqi Gong;Qi Meng;Jue Zhang;H. Qu;Congqiao Li;Sitian Qian;Weitao Du;Zhi-Ming Ma;Tie-Yan Liu
中科院分区：
文献类型：
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作者：
Shiqi Gong;Qi Meng;Jue Zhang;H. Qu;Congqiao Li;Sitian Qian;Weitao Du;Zhi-Ming Ma;Tie-Yan Liu
在粒子物理学中，越来越多地采用深度学习方法来研究喷流。由于在许多应用中，对称性保持行为已被证明是提高深度学习性能的一个重要因素，洛伦兹群等效性（基本粒子的基本时空对称性）最近被纳入了一个用于射流标记的深度学习模型。然而，由于高阶张量的解析构造，该设计计算成本很高。在本文中，我们介绍了LorentzNet，一种新的用于喷气标记的对称性保持深度学习模型。洛伦兹网的信息传递依赖于一个有效的闵可夫斯基点积注意力。在两个具有代表性的喷气标记基准上的实验表明，LorentzNet达到了最佳的标记性能，并且比现有的最先进的算法有了显著的改进。保留洛伦兹对称也大大提高了模型的效率和泛化能力，使LorentzNet在仅训练几千个喷气机时就能达到极具竞争力的性能。
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.