Speaker
Description
The COMET experiment at J-PARC aims to search for the charged lepton flavor violating process of muon-to-electron conversion with unprecedented sensitivity. One of the most serious backgrounds originates from cosmic-ray muons. In particular, a track produced by a backward-going positive muon can mimic the 105 MeV/c signal electron in a cylindrical drift chamber. To address this, we developed a method to identify the track direction based on track-fitting quality metrics using the GENFIT framework. This approach has demonstrated a reduction of the positive muon background by an order of magnitude. In this presentation, we will report on an improved study incorporating machine learning techniques to enhance the performance of track direction identification.
