Charged-particle topology reconstruction with an in-liquid SiPM array

Abstract

Liquid scintillator detectors instrumented with photosensors inside the scintillation volume preserve local optical information that is largely lost in conventional boundary-readout geometries. We demonstrate that this information is sufficient for charged-particle topology reconstruction using a sparse three-dimensional lattice of silicon photomultipliers. After validating the Geant4 detector response against measured photon-count distributions, a simulation-trained, time-informed convolutional neural network reconstructs the entry and exit points of through-going muons with median residuals of 1.91~cm and 2.39~cm, respectively. The reconstructed endpoints are geometrically consistent with acceptance regions defined by external trigger counters in cosmic-ray muon data. The same framework also reconstructs the production vertices of simulated positron starting-track events with a median residual of about 4.5~cm. These results establish the feasibility of topology-sensitive reconstruction using sparse in-liquid photosensor arrays in homogeneous liquid scintillator detectors.