Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors

TL;DR

This paper demonstrates that a graph neural network can effectively improve 3D particle track reconstruction in neutrino detectors by accurately classifying voxels and resolving ambiguities, enhancing the detector's performance.

Contribution

The study introduces a graph neural network based on GraphSAGE for 3D particle track classification in scintillator detectors, showing high efficiency and robustness.

Findings

Voxel classification accuracy of 94-96% per event.

Most ambiguities in particle tracks can be identified and rejected.

The method is robust against systematic effects.

Abstract

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.