We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of particle physics decays and the flexibility of message passing graph neural networks. The Topograph not only solves the combinatoric assignment of observed final state objects, associating them to their original mother particles, but directly predicts the properties of intermediate particles in hard scatter processes and their subsequent decays. In comparison to standard combinatoric approaches or modern approaches using graph neural networks, which scale exponentially or quadratically, the complexity of Topographs scales linearly with the number of reconstructed objects. We apply Topographs to top quark pair production in the all hadronic decay channel, where we outperform the standard approach and match the performance of the state-of-the-art machine learning technique.

翻译：我们提出了一种新方法——拓扑图（Topograph），该方法通过利用粒子物理衰变本质的先验知识以及消息传递图神经网络的灵活性，重构包括中间粒子在内的基础物理过程。拓扑图不仅能解决末态观测粒子的组合分配问题（将其关联至原始母粒子），还能直接预测硬散射过程中中间粒子的性质及其后续衰变行为。与标准组合方法或现代图神经网络方法（复杂度呈指数或二次增长）相比，拓扑图的复杂度随重构对象数量线性增长。我们将拓扑图应用于全强子衰变道的顶夸克对产生过程，在超越标准方法表现的同时，达到了与最先进机器学习技术相当的性能。