  Deep learning tools are being used extensively in high energy physics and are
becoming central in the reconstruction of neutrino interactions in particle
detectors. In this work, we report on the performance of a graph neural network
in assisting with particle flow event reconstruction. The three-dimensional
reconstruction of particle tracks produced in neutrino interactions can be
subject to ambiguities due to high multiplicity signatures in the detector or
leakage of signal between neighboring active detector volumes. Graph neural
networks potentially have the capability of identifying all these features to
boost the reconstruction performance. As an example case study, we tested a
graph neural network, inspired by the GraphSAGE algorithm, on a novel
3D-granular plastic-scintillator detector, that will be used to upgrade the
near detector of the T2K experiment. The developed neural network has been
trained and tested on diverse neutrino interaction samples, showing very
promising results: the classification of particle track voxels produced in the
detector can be done with efficiencies and purities of 94-96% per event and
most of the ambiguities can be identified and rejected, while being robust
against systematic effects.
