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：本文提出一种名为Topograph的新方法，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底层物理过程。Topograph不仅能解决观测末态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属性及其后续衰变行为。相较于传统组合方法或现代图神经网络方法（其复杂度呈指数或二次增长），Topograph的复杂度与重构对象数量呈线性关系。我们将Topograph应用于全强子衰变道的顶夸克对产生过程，其表现超越传统方法，且与当前最先进的机器学习技术性能相当。