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

TL;DR

This paper introduces a Transformer-based pattern recognition method that improves positron track reconstruction in the MEG II experiment, significantly enhancing detection efficiency and resolution under high pileup conditions.

Contribution

The study presents a novel Transformer-based classifier for pileup hit removal, boosting tracking performance in a high-occupancy particle physics detector.

Findings

Hit purity increased significantly

Tracking efficiency improved by 15%

Sensitivity of $% in $$ measurement

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 $5\times 10^7 \mu$/sec. This improvement translates into an approximately 10 % increase in the sensitivity of the $\mu\to e\gamma$ branching ratio measurement.