论文标题
半等级的GNN架构用于喷气标记
Semi-Equivariant GNN Architectures for Jet Tagging
论文作者
论文摘要
已经建议使用尊重物理对称性的操作组成图形神经网络（GNN），以提供更好的模型性能，并具有较少数量的可学习参数。但是，现实世界中的应用程序（例如在高能量物理学中）尚未诞生。我们介绍了新型的结构vecnet，它结合了对称性的和不受约束的操作，以研究和调整物理知识的GNN的程度。我们介绍了一个新颖的度量标准，即\ textit {ant因子}，以量化搜索空间中每种配置的资源效率。我们发现，像我们这样的广义体系结构可以在资源受限的应用程序中提供最佳性能。
Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters. However, real-world applications, such as in high energy physics have not born this out. We present the novel architecture VecNet that combines both symmetry-respecting and unconstrained operations to study and tune the degree of physics-informed GNNs. We introduce a novel metric, the \textit{ant factor}, to quantify the resource-efficiency of each configuration in the search-space. We find that a generalized architecture such as ours can deliver optimal performance in resource-constrained applications.
