Speaker
Description
Accurate particle tracking using the GigaTracker (GTK) silicon pixel detector represents a mission-critical stage in the data processing pipeline of the NA62 experiment at CERN, which is dedicated to the precision measurement of the ultra-rare decay $K^{+} \rightarrow \pi^{+} \nu \bar{\nu}$. Operating in a high-intensity environment with a beam rate of up to 750 MHz, the GTK provides the momentum and direction measurement as well as timing information for the incoming beam particles. Traditional tracking approaches, relying on local combinatorial algorithms, suffer from intrinsic limitations due to high pile-up conditions and the resulting combinatorial background, significantly increasing the rate of fake tracks. In this work, we present the first transformer-based reconstruction algorithm developed specifically for the NA62 GTK, designed to exploit the remarkable single hit time resolution of the detector of $\mathcal{O}$(100 ps). Formulating the tracking challenge within an edge classification framework, the architecture employs a transformer encoder to generate rich, global embeddings of each detector hit's features. These representations are subsequently used to compute connectivity scores between admissible hits across consecutive stations. The models have been trained and extensively validated on high-fidelity Monte Carlo simulation samples, demonstrating excellent generalisation capabilities and robustness across varying beam intensities and data-taking periods. The results show a sharp reduction in the number of false-positive tracks and a substantial increase in purity while maintaining a tracking efficiency comparable to or exceeding the standard algorithm. The architecture is currently used as the default reconstruction method in the NA62 C++ software framework. The improved reconstruction directly translates into enhanced performance for the downstream $K-\pi$ matching task, which is central to background rejection in the experiment.