Erlangen 2026 – scientific programme
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T: Fachverband Teilchenphysik
T 28: Data, AI, Computing, Electronics III
T 28.1: Talk
Tuesday, March 17, 2026, 16:15–16:30, KH 00.024
Multi-Modal track reconstruction using Graph Neural Networks at Belle II — •Tristan Brandes1, Torben Ferber1, Giacomo De Pietro1,2, and Lea Reuter1 — 1Institute of Experimental Particle Physics, Karlsruhe Institute of Technology, Karlsruhe, Germany — 2Scientific Computing Center, Karlsruhe Institute of Technology, Karlsruhe, Germany
Large backgrounds and detector aging impact the track finding in the Belle II central drift chamber, reducing both purity and efficiency in events. This necessitates the development of new track algorithms to mitigate detector performance degradation. Building on our previous success with an end-to-end multi-track reconstruction algorithm for the Belle II experiment at the SuperKEKB collider (arXiv:2411.13596), we have extended the algorithm to incorporate inputs from both the drift chamber and the silicon vertex tracking detector, creating a multi-modal network. We employ graph neural networks to handle the irregular detector structure and object condensation to address the unknown, varying number of particles in each event. This approach simultaneously identifies all tracks in an event and determines their respective parameters.
We have fully integrated this algorithm into the Belle II analysis software framework. Utilizing a realistic full detector simulation, which includes beam-induced backgrounds and detector noise derived from actual collision data, we report the performance of our track-finding algorithm across various event topologies compared to the existing baseline algorithm used in Belle II.
Keywords: Tracking; Graph Neural Networks; Machine Learning; Drift Chamber; Silicon Vertex Detector