论文标题
部分可观测时空混沌系统的无模型预测
Do graph neural networks learn traditional jet substructure?
论文作者
论文摘要
在CERN LHC上，喷气标记的任务是推断出给定一组最终态粒子的喷气机的起源，由机器学习方法主导。图形神经网络已用于通过将喷气机视为粒子之间的基础，可学习的边缘连接来解决此任务。我们通过寻找使用LayerWise-Relevance繁殖技术识别的相关边缘连接来探索一个这样最先进的网络Priendlenet的决策过程。随着模型的训练，我们观察到连接不同颗粒中间簇的相关边缘的分布变化，称为子播种。源自顶级夸克的信号喷气机的征射击连接的分布不同，该Quark通常与其三种衰减产物相对应，而背景喷头则来自较轻的夸克和胶子。此行为表明该模型使用传统的Jet子结构可观察物，例如在识别喷气机时在喷气机内使用的插脚数量（能量粒子簇）。
At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particles inside. We explore the decision-making process for one such state-of-the-art network, ParticleNet, by looking for relevant edge connections identified using the layerwise-relevance propagation technique. As the model is trained, we observe changes in the distribution of relevant edges connecting different intermediate clusters of particles, known as subjets. The resulting distribution of subjet connections is different for signal jets originating from top quarks, whose subjets typically correspond to its three decay products, and background jets originating from lighter quarks and gluons. This behavior indicates that the model is using traditional jet substructure observables, such as the number of prongs -- energetic particle clusters -- within a jet, when identifying jets.
