A GNN-based end-to-end track finder for the Belle II drift chamber reconstructs displaced tracks at 85.4% efficiency with a 2.5% fake rate, outperforming the baseline algorithm at 52.2%.
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End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II
A GNN-based end-to-end track finder for the Belle II drift chamber reconstructs displaced tracks at 85.4% efficiency with a 2.5% fake rate, outperforming the baseline algorithm at 52.2%.