论文标题
高效的Lorentz Eporiant图形神经网络，用于喷射标记
An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
论文作者
论文摘要
深度学习方法已越来越多地用于研究粒子物理学中的喷气机。由于已经证明具有对称性的具有对称性的行为是改善许多应用中深度学习性能的重要因素，因此Lorentz Group Equivariance（用于基本粒子的基本时空对称性）最近已被纳入用于喷气标记的深度学习模型中。但是，由于高阶张量的分析构建，该设计在计算上是昂贵的。在本文中，我们介绍了Lorentznet，这是一种用于喷气标记的新型对称性深度学习模型。 Lorentznet的消息传递取决于有效的Minkowski点产品的关注。在两个代表性的喷气标记基准上进行的实验表明，Lorentznet实现了最佳的标记性能，并且在现有的最新算法中可以显着改善。 Lorentz对称性的保留也大大提高了模型的效率和概括能力，从而使Lorentznet在仅接受几千架喷气机的训练时就能达到高度竞争性的性能。代码和型号可在\ url {https://github.com/sdogsq/lorentznet-release}中获得。
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 \url{https://github.com/sdogsq/LorentzNet-release}.
