Extracting Signal Electron Trajectories in the COMET Phase-I Cylindrical Drift Chamber Using Deep Learning

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 $40\mathrm{\%}$ occupancy of the total CDC cells, utilizing deep learning techniques of semantic segmentation. Our model achieved remarkable results, with a purity rate of $98\mathrm{\%}$ and a retention rate of $90\mathrm{\%}$ for CDC cells with signal hits, surpassing the design-goal performance of $90\mathrm{\%}$ 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.