BibTex

File contents

@article{10.1093/ptep/ptaf048,
    author = {Kaneko, Fumihiro and Kuno, Yoshitaka and Sato, Joe and Sato, Ikuya and Pieters, Dorian and Wu, Chen},
    title = {Extracting Signal Electron Trajectories in the COMET Phase-I Cylindrical Drift Chamber Using Deep Learning},
    journal = {Progress of Theoretical and Experimental Physics},
    volume = {2025},
    number = {5},
    pages = {053C01},
    year = {2025},
    month = {04},
    abstract = {We present a pioneering approach to tracking analysis within the Coherent Muon to Electron Transition (COMET) Phase-I experiment, which aims to search for the charged lepton flavor violating \$\\mu \\rightarrow 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) amid 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.},
    issn = {2050-3911},
    doi = {10.1093/ptep/ptaf048},
    url = {https://doi.org/10.1093/ptep/ptaf048},
    eprint = {https://academic.oup.com/ptep/article-pdf/2025/5/053C01/63211412/ptaf048.pdf},
}