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——Topograph，该方法通过利用粒子物理衰变本质的底层先验知识以及消息传递图神经网络的灵活性，重构包含中间粒子的底层物理过程。Topograph不仅能解决观测末态物体的组合分配问题，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及其后续衰变的属性。与标准组合方法或采用图神经网络的现代方法相比（其复杂度呈指数或二次增长），Topograph的复杂度随重构物体数量线性增长。我们将Topograph应用于全强子衰变道的顶夸克对产生过程，在性能上优于标准方法，并与现有最先进的机器学习技术相当。