Graph neural networks match state-of-the-art performance for positron energy regression and neutron tagging in a Hyper-Kamiokande-like water-Cherenkov detector
This work applies message-passing graph neural networks to Inverse Beta Decay detection of the Diffuse Supernova Neutrino Background in water-Cherenkov detectors, building a pipeline that reconstructs low-energy events from raw photomultiplier hits: one model regresses 3–30 MeV positron energy and another classifies neutron-capture signals, achieving performance comparable to the state of the art on simulations in a Hyper-Kamiokande-like geometry.