Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays

TL;DR

This paper presents a graph neural network-based method for accurately reconstructing the direction and energy of ultra-high-energy cosmic rays from radio detector data, incorporating physical knowledge to improve precision and robustness.

Contribution

The authors introduce a novel GNN architecture that integrates physical insights, reducing training data needs and enhancing the reliability of cosmic-ray parameter reconstruction.

Findings

Achieves 0.092° angular resolution

Reconstructs electromagnetic energy with 16.4% accuracy

Incorporates uncertainty estimation for confidence intervals

Abstract

Using advanced machine learning techniques, we developed a method for reconstructing precisely the arrival direction and energy of ultra-high-energy cosmic rays from the voltage traces they induced 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{\deg} and an electromagnetic energy reconstruction resolution of 16.4% on simulated data with realistic noise conditions. We also employ uncertainty estimation methods to enhance the reliability of our predictions, quantifying the confidence of the GNN's outputs and providing confidence intervals for both direction and energy reconstruction. Finally, we investigate strategies to verify the model's consistency and robustness under real life variations, with the goal of identifying scenarios in which predictions remain reliable despite domain shifts between simulation and reality.