Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks

תקציר מקורי באנגליתarXiv:2602.23321v2 Announce Type: replace-cross Abstract: Using advanced machine learning techniques, we developed a method to reconstruct the arrival direction and energy of ultra-high-energy cosmic rays from the voltage traces they induce on ground-based radio detector arrays. In our approach, triggered antennas are represented as a graph structure, which serves as input for a graph neural network (GNN). By incorporating physical knowledge into both the GNN architecture and the input data, we improve the precision and reduce the required size of the training set with respect to a fully data-driven approach. This method achieves an angular resolution of 0.092 degrees and an electromagnetic energy reconstruction resolution of 16.4% on simulated data with realistic noise conditions. We also