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.

我们提出了一种新的方法--拓扑图，它重建了底层 物理过程，包括中间粒子，通过利用 粒子物理本质的潜在先验衰变和 消息传递图神经网络的灵活性。地形图不仅仅是 解决观察到的最终状态对象的组合赋值，关联 它们与它们原始的母粒子，但直接预测的性质 硬散射过程中的中间粒子及其后续 腐烂。与标准的组合方法或现代方法相比 使用图形神经网络，它以指数或二次方式扩展， 地形的复杂性与重建的数量成线性关系 物体。 我们将拓扑图应用于全强子衰变中顶夸克对的产生 渠道，其中我们的表现优于标准方法，并与 最先进的机器学习技术。