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中的先验知识以及消息传递图神经网络的灵活性，重建包含中间粒子的底层物理过程。Topograph不仅能解决观测末态粒子的组合分配问题，将它们关联到各自的母粒子，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。与传统的组合方法或使用图神经网络的现代方法（复杂度分别呈指数级和二次方增长）相比，Topograph的复杂度随重建对象数量呈线性增长。我们将Topograph应用于全强子衰变道的顶夸克对产生事例，在该任务中我们超越了传统方法，并达到了与当前最先进的机器学习技术相当的性能。