Speaker
Description
High levels of beam-induced detector noise and detector aging degrade track-finding performance in the Belle II central drift chamber, resulting in losses of both track finding efficiency and purity. This motivates the development of reconstruction approaches capable of maintaining robust performance under deteriorating detector conditions. Building on our earlier work on an end-to-end multi-track reconstruction method for Belle II at the SuperKEKB collider (arXiv:2411.13596), we have expanded the algorithm to utilise information from both the drift chamber and the silicon vertex detector simultaneously, creating a multi-modal network. Graph neural networks are used to accommodate the irregular detector geometry, while object condensation enables reconstruction in the presence of an unknown and variable number of charged particles per event. The resulting model reconstructs all tracks in an event simultaneously and estimates their corresponding parameters.
We demonstrate the algorithm's effectiveness using a realistic full detector simulation, which incorporates beam-induced backgrounds and noise modeled from actual collision data. The simultaneous reconstruction of the information from the two detectors significantly improves the track purity while maintaining comparable efficiency. We provide a detailed comparison of its track-finding performance against the current Belle II baseline across various event topologies. Finally, we address the practical implementation by detailing the network's integration into the Belle II analysis software framework via ONNX, discussing critical challenges like model conversion, inference speed, memory usage, and ensuring compatibility with existing reconstruction workflows.