在 Belle II 中使用基于图的多模态轨道重建减轻探测器老化效应
Mitigating Detector Ageing Effects with Graph-Based Multi-Modal Track Reconstruction at Belle II
AI总结：
研究 Belle II 探测器老化对轨道寻找的影响，采用基于图神经网络的算法，将轨道寻找设为全局关系聚类问题，经全探测器模拟评估性能，结果显示该算法在探测器老化时能提高鲁棒性，稳定跟踪性能。
AI中文摘要：
大背景会导致硬件故障和探测器增益下降，影响 Belle II 中央漂移室中的轨道寻找。这些情况导致空间上不均匀且随时间变化的低效率，产生无效区域和缺失命中，挑战传统跟踪算法。本文评估了先前开发的基于统一图神经网络（GNN）的轨道寻找算法在实际长期探测器老化条件下的性能。将轨道寻找表述为使用对象凝聚的全局关系聚类问题，能重建每个事件中未知且可变数量的轨道。利用包含束流诱导背景、探测器噪声和实测探测器老化效应的实际全探测器模拟，评估跟踪性能并与当前 Belle II 基线重建进行比较。结果表明探测器退化可视为观测命中模式中的域转移，而非需要全新重建策略。在退化探测器条件下重新训练后，基于 GNN 的方法将均匀位移μ子的绝对轨道效率损失限制在 14%，而基线跟踪为 28%，同时保持 96%的轨道纯度，而基线重建在相同条件下仅达到 90%的轨道纯度。这些结果证明基于统一 GNN 的重建对不规则命中模式和扩展的无效区域具有更高的鲁棒性，能在 Belle II 探测器长期老化下实现稳定跟踪性能。
英文摘要：
Large backgrounds that can lead to hardware failures and the degradation of detector gain impact the track finding in the Belle II central drift chamber. These conditions lead to spatially non-uniform and time-dependent inefficiencies, which results in inactive regions and missing hits which challenges conventional tracking algorithms and necessitate the development of new track finding algorithms. In this work, we evaluate the performance of our previously developed unified graph neural network (GNN) based track-finding algorithm under realistic long-term detector ageing conditions. Track finding is formulated as a global relational clustering problem using object condensation, which enables the reconstruction of an unknown and variable number of tracks per event. Using a realistic full detector simulation incorporating beam-induced backgrounds, detector noise, and measured detector ageing effects, we evaluate the tracking performance and compare it to the current Belle II baseline reconstruction. We show that detector degradation can be treated as a domain shift in the observed hit patterns, rather than requiring a fundamentally new reconstruction strategy. After retraining on degraded detector conditions, the GNN-based approach limits the absolute track efficiency loss for uniformly displaced muons to 14%, compared to 28% for the baseline tracking, while maintaining a track purity of 96%. Under the same conditions, the baseline reconstruction achieves only 90% track purity. These results demonstrate that the unified GNN-based reconstruction provides increased robustness to irregular hit patterns and extended inactive regions, enabling stable tracking performance under long-term detector ageing at Belle II.
