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

Excerpt

arXiv: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