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.
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BibTeX
@misc{kimku2026chargedparticle,
title = {{Charged-particle topology reconstruction with an in-liquid SiPM array}},
author = {H. Kimku and J. S. Chung and C. Ha and T. Z. Huang and J. Kim and S. Kim and B. C. Koh and M. S. Kwak and S. Lee and Y. J. Lee and J. Seo},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25532},
eprint = {2609.25532},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25532},
}
RIS
TY - UNPB
TI - Charged-particle topology reconstruction with an in-liquid SiPM array
AU - H. Kimku
AU - J. S. Chung
AU - C. Ha
AU - T. Z. Huang
AU - J. Kim
AU - S. Kim
AU - B. C. Koh
AU - M. S. Kwak
AU - S. Lee
AU - Y. J. Lee
AU - J. Seo
PY - 2026
DA - 2026/09/24
PB - arXiv
DO - 10.48550/arxiv.2609.25532
UR - https://arxiv.org/abs/2609.25532
AB - 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.
ER -