论文标题
用于喷气标记的模棱两可的能量流网络
Equivariant Energy Flow Networks for Jet Tagging
论文作者
论文摘要
使用深度学习的喷气标记技术显示出改善山脉物理分析的巨大潜力。一种这样的方法是能量流网络（EFN） - 最近引入的神经网络结构，将喷气机表示为置换式粒子矩阵集合集，同时保持红外和界线安全性。我们基于深处的形式主义，开发了能量流网络体系结构的变体，并结合了置换量表。我们得出了可以维持红外和共线安全性的条件，并在W-Boson标记的规范示例中研究这些网络的性能。我们发现，地位能量流网络的性能与粒子流网络相似，粒子流网络优于标准EFN。然而，地位粒子流网络遇到了融合和过度拟合问题。最后，我们研究了地位网络如何雕刻喷气质量，并在使用刨花的情况下提供一些关于去相关的初始结果。
Jet tagging techniques that make use of deep learning show great potential for improving physics analyses at colliders. One such method is the Energy Flow Network (EFN) - a recently introduced neural network architecture that represents jets as permutation-invariant sets of particle momenta while maintaining infrared and collinear safety. We develop a variant of the Energy Flow Network architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. We derive conditions under which infrared and collinear safety can be maintained, and study the performance of these networks on the canonical example of W-boson tagging. We find that equivariant Energy Flow Networks have similar performance to Particle Flow Networks, which are superior to standard EFNs. However, equivariant Particle Flow Networks suffer from convergence and overfitting issues. Finally, we study how equivariant networks sculpt the jet mass and provide some initial results on decorrelation using planing.
