Artificial Intelligence techniques for space experiments

The application of advanced Artificial Intelligence (AI) techniques in astroparticle experiments represents a groundbreaking advancement in data analysis and experimental design. As space missions become increasingly complex, integrating AI technologies is essential for optimizing their performance and enhancing their scientific outcomes. This study focuses on the use of Graph Neural Networks (GNNs) for tracking systems and Transformers for calorimeters in space-based experiments. A significant challenge for trackers in space experiments is the high-noise environment, characterized mainly by backscattering tracks. We will present a novel approach that employs GNNs for node-level tasks, specifically aimed at classifying backscattering hits versus signal hits and reconstructing tracks. The goal for the space calorimeter experiment is to distinguish between particle types, such as electrons and protons. By capturing the dependencies within these features, Transformers can achieve robust classifications, even when the data spans thousands of channels or dimensions. By addressing these challenges, we aim to enhance the accuracy and reliability of data interpretation in astroparticle physics through the application of advanced Artificial Intelligence techniques.