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过程的固有先验知识以及消息传递图神经网络的灵活性，重建包括中间粒子在内的底层物理过程。Topograph 不仅解决了观测未态粒子的组合分配问题，将其与原始母粒子相关联，还能直接预测硬散射过程中间粒子及其后续衰变的属性。与指数级或二次方复杂度的标准组合方法或现代图神经网络方法相比，Topograph 的计算复杂度随重建对象数量线性增长。我们将 Topograph 应用于全强子衰变道中的顶夸克对产生过程，其性能优于标准方法，并达到了当前最先进机器学习技术的水平。