Speaker
Description
Accurate energy reconstruction is a key challenge in neutrino telescopes, where the total neutrino energy is not fully contained in the detector and relying only on the reconstructed muon energy neglects the contribution from hadronic showers. We present a Graph Neural Network (GNN)-based approach for visible energy reconstruction in KM3NeT/ARCA using the DynEdge architecture within the GraphNeT framework, with visible energy as the reconstruction target.
This first implementation on ARCA with 21 detection units is trained on simulated νμ charged-current events using raw hit-level detector information. The results demonstrate stable performance across different simulation versions and generalization to all neutrino flavors despite training on a single flavor sample. We further investigate training strategies including both neutrino interactions and atmospheric muon backgrounds, revealing a domain adaptation challenge: while the inclusion of atmospheric muons improves their reconstruction, it introduces a degradation in neutrino performance. Understanding and mitigating such effects is essential for robust machine learning (ML) applications in realistic experimental environments. Finally, we discuss initial steps towards interpreting GNN predictions and understanding the detector information driving the reconstruction, an important step towards building trust in ML-based methods for neutrino physics.