发表机构
Institute of High Energy Physics, Chinese Academy of Sciences; University of Chinese Academy of Sciences; China Academy of Space Technology（中国科学院高能物理研究所; 中国科学院大学; 中国空间技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合 BERT 编码器与 Transformer 解码器的模型，通过自回归排序实现漂移室击中到径迹的关联，在 DCTracks 数据集上取得高效率和低假率，适用于低本底高精度实验。
AI 中文摘要
漂移室中的径迹重建对于正负电子对撞机上的动量测量和粒子鉴别至关重要。虽然 Transformer 架构已经改变了许多序列处理领域，但其在高能物理径迹重建中的应用仍在探索之中。我们提出了一种将 BERT 编码器与 Transformer 解码器相结合的模型，通过自回归排序实现击中到径迹的关联。该模型在 DCTracks 开放数据集上进行了评估，该数据集提供了包含不同粒子类型、动量、径迹多重性和噪声条件的真实漂移室模拟。在单径迹、双径迹和多径迹样本中，该模型在保持克隆和假径迹率极低的同时，实现了较高的击中效率和径迹效率。该模型在位移顶点的重建中也表现出色。这些结果表明，基于 BERT 的序列到序列模型是低本底、高精度实验中径迹重建的一种有前景的方法。
英文摘要
Track reconstruction in drift chambers is essential for momentum measurement and particle identification at electron-positron colliders. While Transformer architectures have transformed many sequence-processing domains, their application to tracking in high energy physics is still being explored. We present a model that combines a BERT encoder with a Transformer decoder to perform hit-to-track association through autoregressive sorting. The model is evaluated on the DCTracks open dataset that provides realistic drift chamber simulations with varying particle types, momenta, track multiplicities, and noise conditions. Across single-track, two-track, and multi-track samples, the model achieves high hit and track efficiencies while keeping the rates of clones and fakes very low. It also works well in the reconstruction of displaced vertices. These results show BERT-based sequence-to-sequence models as a promising approach for track reconstruction in low-background, precision-oriented experiments.
