Pattern Recognition with Artificial Intelligence in Space Experiments

The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a major advancement in both data analysis and experimental design. As space missions become increasingly complex, integrating AI tools is essential for optimizing system performance and maximizing scientific return. This study explores the use of Graph Neural Networks (GNNs) within the tracking systems of space-based experiments. A key challenge in track reconstruction is the high level of noise, primarily due to backscattering tracks, which can obscure the identification of primary particle trajectories. We propose a novel GNN-based approach for node-level classification tasks, specifically designed to distinguish primary tracks from backscattered ones within the tracker. In this framework, AI is employed as a powerful tool for pattern recognition, enabling the system to identify meaningful structures within complex tracking data and to discriminate signal from backscattering with higher precision. By addressing these challenges, our work aims to enhance the accuracy and reliability of data interpretation in astroparticle physics through the advanced deep learning techniques.