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%.
Extracting Signal Electron Trajectories in the COMET Phase-I Cylindrical Drift Chamber Using Deep Learning
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abstract
We present a pioneering approach to tracking analysis within the COMET Phase-I experiment, which aims to search for the charged lepton flavor violating $\mu\to e$ conversion process in a muonic atom, at J-PARC, Japan. This paper specifically introduces the extraction of signal electron trajectories in the COMET Phase-I cylindrical drift chamber (CDC) amidst a high background hit rate, with more than $40\,\%$ occupancy of the total CDC cells, utilizing deep learning techniques of semantic segmentation. Our model achieved remarkable results, with a purity rate of $98\,\%$ and a retention rate of $90\,\%$ for CDC cells with signal hits, surpassing the design-goal performance of $90\,\%$ for both metrics. This study marks the initial application of deep learning to COMET tracking, paving the way for more advanced techniques in future research.
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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%.