Speaker
Description
KM3NeT/ARCA and KM3NeT/ORCA are the new generation of neutrino telescopes located
in the depths of the Mediterranean Sea. Each comprises a grid of optical sensors that
capture the Cherenkov light emitted by charged particles produced in neutrino interactions.
KM3NeT/ARCA, sensitive to interactions with energies ranging from TeV to PeV, focuses on
cosmic neutrinos, while KM3NeT/ORCA investigates atmospheric neutrino oscillations in the
GeV energy range.
Among various approaches, Graph Neural Networks (GNNs) stand out as a promising
method to reconstruct different observables in neutrino interactions. GraphNeT, a software
which uses several GNN-based models, have demonstrated competitive performance with
respect to likelihood-based reconstruction algorithms in IceCube. This contribution will
provide an overview of the latest developments within the GraphNet software. Introducing a
new database developed for handling KM3NeT data, we will present preliminary results on
direction, energy, and position reconstructions, along with topology classification for ORCA6
and ORCA115.
