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.

Figures (8)

• Figure 1: Transformer model adapted to the MEG II positron spectrometer. Two sets of queries, pTC hits and CDCH hits, are input to the model. The model outputs the probability of each CDCH hit being associated with the pTC hits.
• Figure 2: Distribution of hit positions for an example 1.5-turn positron track, shown in conformal coordinates. The left panel shows the true hit positions, where hits in the same turn segment are well aligned. The right panel shows the reconstructed hit positions with finite detector resolution, where CDCH hit alignment deteriorates.
• Figure 3: Distribution of $\phi$ vs. $z$ used for $\phi_{\mathrm{turn};s}$ and $z_{\mathrm{turn};s}$ calibration, shown for different combinations of layer and $s$ index. Track-fitted $\phi$ and $z$ coordinates of each hit are used in these plots. The left (right) three plots correspond to the innermost (outermost) layer.
• Figure 4: Correlation between signal-hit efficiency and false positive rate for pileup in the validation dataset at 5e7μ/sec. The red marker indicates the values at the adopted ML output threshold.
• Figure 5: Model outputs for an example 1.5-turn track in the validation dataset. The left plots show the model predictions, which can be compared with the labels according to the MC truth displayed on the right.