论文标题
用图网络在隆德平面上标记
Jet tagging in the Lund plane with graph networks
论文作者
论文摘要
在大型强子对撞机的实验研究中，鉴定增强的重颗粒（例如顶级夸克或载体玻色子）是产生的关键问题之一。在本文中，我们介绍了Lundnet，这是一种新型的喷气标记方法，依赖于图形神经网络以及对喷气机中的辐射模式的有效描述，以最佳地脱离了来自背景事件的升压对象的签名。我们将此框架应用于许多不同的基准测试，与现有最新算法相比，顶部标记的性能显着提高。我们研究Lundnet标记器对非扰动和检测器效应的鲁棒性，并显示Lund平面中的运动学切割如何减轻对模型依赖性贡献的过度拟合。最后，我们将此方法的计算复杂性及其缩放视为运动学隆德平面切割的函数，显示了比以前的基于图的标签器的速度的数量级提高。
The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions. Finally, we consider the computational complexity of this method and its scaling as a function of kinematic Lund plane cuts, showing an order of magnitude improvement in speed over previous graph-based taggers.
