Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II

Abstract

We developed a Transformer-based pattern recognition method for positron track reconstruction in the MEG II experiment. The model acts as a classifier to remove pileup hits in the MEG II drift chamber, which operates under a high pileup occupancy of 35 - 50 %. The trained model significantly improved hit purity, leading to enhancements in tracking efficiency and resolution by 15 % and 5 %, respectively, at a muon stopping rate of $5\times 10^7 μ$/sec. This improvement translates into an approximately 10 % increase in the sensitivity of the $μ\to eγ$ branching ratio measurement.