用迭代图模型重建高能对撞中短寿命粒子，提升精度。
Pairton: Iterative Reconstruction of Short-Lived Particles
- 将粒子重构建模为图结构的掩码预测，迭代优化衰变关系。
- 在全强子底夸克-反夸克对事件中达到当前最优性能。
- 适合需要高精度粒子重构的物理分析与生成模型研究者。
我们提出 Pairton，一种用于高能对撞事件中短寿命粒子重构的迭代框架。通过将粒子重构建模为图结构上的掩码预测过程，Pairton 学习与衰变产物因子分解一致的条件分布，并迭代预测表示粒子衰变关系的邻接矩阵中的边。采用基于 pairformer 的架构，结合动态更新的成对表示，方法引入全局事件一致性。在全强子 $t\bar{t}$ 衰变任务上展示出当前最优性能。Pairton 提供了一种通用、灵活的粒子重构范式，可轻松扩展至其他衰变拓扑，融合现代生成建模与高能物理的思想。
原文摘要 · Abstract (English)
We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events. By formulating particle reconstruction as a masked prediction process over graph structures, Pairton learns conditional distributions consistent with a factorised decomposition of decay products and iteratively predicts edges in the adjacency matrix representing particle decay relationships. Leveraging a pairformer-based architecture with dynamically updated pairwise representations, our method incorporates global event consistency. We demonstrate state-of-the-art performance on fully hadronic $t\bar{t}$ decays. Pairton provides a general, flexible paradigm for particle reconstruction and can be readily extended to other topologies, bridging ideas from modern generative modelling and high-energy physics.
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