标题： 基于图神经网络的STCF漂移室噪声过滤算法 显示英文标题

标题： Noise Filtering Algorithm Based on Graph Neural Network for STCF Drift Chamber

摘要： 超级$\tau$-charm 设施（STCF）是中国提出的一种高亮度的下一代正负电子对撞机。 更高的亮度导致背景水平增加，这对带电粒子的轨迹重建提出了重大挑战。 特别是在低横向动量区域，当前的轨迹重建算法明显受到背景的影响，导致重建效率不佳和虚假率较高。 为了解决这一挑战，我们提出了一种基于图神经网络（GNN）的噪声过滤算法（GNF 算法），作为轨迹重建的预处理步骤。 GNF 算法引入了一种将探测器数据转换为图的新方法，并应用分层阈值策略，将基于 GNN 的边分类结果映射到信号-噪声分离。 基于蒙特卡罗（MC）数据的研究表明，实施 GNF 算法后，标准背景下的重建效率与无背景情况相当，而虚假率显著降低。 因此，GNF 算法为 STCF 轨迹软件提供了重要支持。 显示英文摘要

摘要： The super $\tau$-charm facility (STCF) is a next-generation electron-positron collider with high luminosity proposed in China. The higher luminosity leads to increased background level, posing significant challenges for track reconstruction of charged particles. Particularly in the low transverse momentum region, the current track reconstruction algorithm is notably affected by background, resulting in suboptimal reconstruction efficiency and a high fake rate. To address this challenge, we propose a Graph Neural Network (GNN)-based noise filtering algorithm (GNF Algorithm) as a preprocessing step for the track reconstruction. The GNF Algorithm introduces a novel method to convert detector data into graphs and applies a tiered threshold strategy to map GNN-based edge classification results onto signal-noise separation. The study based on Monte Carlo (MC) data shows that with the implementation of the GNF Algorithm, the reconstruction efficiency with the standard background is comparable to the case without background, while the fake rate is significantly reduced. Thus, GNF Algorithm provides essential support for the STCF tracking software.