Joint Direction and Distance Estimation of Low-Energy Photon Point Sources Using a Spatial–Global Dual-Branch CNN

An uncollimated scintillator array encodes source direction and distance in both its spatial response and total count rate. We modeled an isotropic 11.6 keV point source and a 12 × 12 yttrium orthosilicate array in Geant4 11.3.2, using a scintillation yield of 15,300 photons MeV−1. A spatial–global dual-branch convolutional neural network, Full, combined normalized response patterns with six global descriptors for joint localization conditional on known emitted exposure. The response library comprised 272 training and 96 validation positions, with a 72-position multi-distance evaluation grid and 18 fixed-distance diagnostic positions. On the multi-distance grid, Full achieved three-seed mean directional and distance MAEs of 10.4° and 26.0 mm. At 300 randomly sampled positions and 4 million primaries, the corresponding means were 15.6° and 52.6 mm. An input-matched multilayer perceptron gave 16.7°/55.2 mm at 4 million primaries. Same-coordinate exposure controls and independent Monte Carlo repetitions characterized statistical sensitivity. A forward-response inverse estimator performed better in a far-field, near-axis subset. The results support combining spatial and rate information for localization under the simulated conditions and identify exposure and viewing geometry as major limits on accuracy.