Speaker
Atsushi Oya
(The university of Tokyo)
Description
In the MEG II experiment, which searches for $\mu\to e\gamma$, a cylindrical drift chamber measures positrons from muon decays. A key challenge arises from the declining positron reconstruction efficiency in the high-pileup environment, primarily due to algorithm limitations. To address this, a machine learning-based noise filtering technique has been developed. This presentation introduces the ML model architecture and its application, followed by a discussion on improvements in tracking performance.
Author
Atsushi Oya
(The university of Tokyo)