论文标题
深度学习以像素和带状粒子探测器中的轨道识别
Deep learning for track recognition in pixel and strip-based particle detectors
论文作者
论文摘要
在跟踪探测器中，带电颗粒轨迹的重建是分析高能和核物理实验数据的关键问题。现代实验中的数据量是如此之大，以至于经典的跟踪方法（例如Kalman滤镜）无法足够快地处理它们。为了解决这个问题，我们基于深度学习体系结构开发了两种神经网络算法，以在基于像素和带状的粒子探测器中进行音轨识别。这些是本地（按轨道轨道）的TrackNetv3，对于全局（事件中的所有轨道）跟踪的rdgraphnet。使用Jinr（Dubna）的BM@N实验的GEM跟踪器和IHEP CAS（北京）BESIII实验的圆柱GEM内部追踪器测试了这些算法。基于反向定向图的RDGRAPHNET模型显示出令人鼓舞的结果：95％的召回率和74％的轨道查找精度。 TrackNETV3模型显示出95％和76％精度的召回值。在进一步的模型优化之后，可以改善此结果。
The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high-energy and nuclear physics. The amount of data in modern experiments is so large that classical tracking methods, such as the Kalman filter, cannot process them fast enough. To solve this problem, we developed two neural network algorithms based on deep learning architectures for track recognition in pixel and strip-based particle detectors. These are TrackNETv3 for local (track by track) and RDGraphNet for global (all tracks in an event) tracking. These algorithms were tested using the GEM tracker of the BM@N experiment at JINR (Dubna) and the cylindrical GEM inner tracker of the BESIII experiment at IHEP CAS (Beijing). The RDGraphNet model, based on a reverse directed graph, showed encouraging results: 95% recall and 74% precision for track finding. The TrackNETv3 model demonstrated a recall value of 95% and 76% precision. This result can be improved after further model optimization.
