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.