Speaker
Description
The Giant Radio Array for Neutrino Detection (GRAND) aims to detect and study ultra-high-energy (UHE) neutrinos by observing the radio emission produced in extensive air showers. The GRANDProto300 prototype focuses primarily on UHE cosmic rays to demonstrate the autonomous detection and reconstruction techniques that will later be applied to neutrino detection. In this work, we propose a method for reconstructing the arrival direction, energy, and nature of primary particle with high precision using state-of-the-art machine-learning techniques from noisy simulated voltage traces.
For each simulated event, we represent the triggered antennas as a graph structure, which is used as input for training a graph neural network (GNN). To significantly enhance precision and reduce the required training set size, we incorporate physical knowledge into both the GNN architecture and the input data. This approach achieves an angular resolution of 0.07° and a primary energy reconstruction resolution of less than 20%. Additionally, we employ uncertainty estimation methods to improve the reliability of our predictions. These methods allow us to quantify the confidence of the GNN predictions and to provide confidence intervals for the direction and energy reconstruction.
| Collaboration(s) | GRAND |
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