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