ACAT 2025

End-to-End MDC Track Reconstruction using Graph Neural Networks at BESIII

Not scheduled

30m

Hamburg, Germany

Poster Track 2: Data Analysis - Algorithms and Tools Poster session with coffee break

Speaker

Liyan Qian (Chinese Academy of Sciences (CN))

Description

We present an end-to-end track reconstruction algorithm based on Graph Neural Networks (GNNs) for the main drift chamber of the BESIII experiment at the BEPCII collider. The algorithm directly processes detector hits as input to simultaneously predict the number of track candidates and their kinematic properties in each event. By incorporating physical constraints into the model, the reconstruction efficiency achieves parity with or surpasses traditional methods. Further improvements are anticipated as the research progresses.