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

Source: [Extracting Signal Electron Trajectories in the COMET Phase-I Cylindrical Drift Chamber Using Deep Learning](<https://www.sota2.com/research/paper/68fa48b01e209131ce931649>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T06:57:36.865Z

Authors: Fumihiro Kaneko, Yoshitaka Kuno, Joe Sato, Ikuya Sato, Dorian Pieters, Chen Wu

Published: 2024-08-09T00:23:04.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2408.04795>)

[PDF](<https://arxiv.org/pdf/2408.04795.pdf>)