Particle Tracking in the NA62 Experiment

The NA62 experiment at the Super Proton Synchrotron at CERN aims to measure the branching ratio of the ultra-rare channel 𝐾 + → 𝜋+ 𝜈𝜈¯ with a predicted BR = (8.6 ± 0.42) × 10^ −11. The GigaTracker, a four-layer silicon-pixel tracker, measures the momentum and direction of incoming hadron beam particles at a rate of 750 MHz. The experiment’s success relies on effectively managing pile-up, temporal matching and vertex reconstruction between beam kaons and decay pions, a challenging task due to the current combinatorial track-building approach. This research integrates machine learning best practices in the current modeling strategies applied in the NA62 experiment. In this study, we extensively evaluate different deep learning architectures for particle tracking, showing the most effective approaches. In particular, we designed and implemented three distinct strategies based on Multi-Layer Perceptron, Transformer and Graph Neural Network. All methods were evaluated using efficiency, purity and fake-track rate. The most promising ones were further assessed in an aggregation-free setup, where clustering based on ground truth information was removed to test their robustness in a real-world scenario.