Speakers
Description
We present an end-to-end track reconstruction algorithm based on Graph Neural Networks (GNNs) for a 35 layers multilayer drift chamber (MDC) combined with a 3 layers cylindrical gas electron multiplier (CGEM) in the BESIII experiment at the BEPCII collider. The algorithm directly processes MDC wire measurement and CGEM cluster as input to simultaneously predict the number of track candidates and their kinematic properties in each event. The reconstruction efficiency achieves parity with or surpasses traditional methods, demonstrating marked improvement for low-momentum samples. In addition to the track parameters, the detailed information of hit, such as position, momentum, flight length and left-right ambiguity, can all be predicted. Further improvements are anticipated as the research progresses.