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arXiv · 2512.19482

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

Lapo Dispoto·Fedor Ignatov·Atsushi Oya·Yusuke Uchiyama·Antoine Venturini
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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.

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Lapo Dispoto, Fedor Ignatov, Atsushi Oya, Yusuke Uchiyama, Antoine Venturini. 2025-12-22. Transformer-Based Approach to Enhance Positron Tracking Performance in MEG II. https://doi.org/10.1016/j.nima.2026.171817

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