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

Direction and distance affect both the magnitude and spatial distribution of counts in a compact scintillator array, which makes their simultaneous estimation difficult. We modeled radiation transport and detector response in Geant4 11.3.2 for a 12 × 12 yttrium orthosilicate (YSO) array irradiated by an isotropic 11.6 keV photon point source. The analysis assumes a known emitted exposure. A dual-branch convolutional neural network combined the normalized 12 × 12 response with six global descriptors—the total photon rate, two centroid coordinates, two spatial spreads, and covariance—to estimate a front-hemisphere unit direction vector and the source-to-detector distance. The dataset contained 272 training, 96 validation, and 72 in-dependent test positions. Across three initializations, the full model achieved a polar-angle MAE of 5.16 ± 0.42°, circular azimuth MAE of 13.56 ± 0.85°, three-dimensional directional MAE of 9.68 ± 0.84°, distance MAE of 26.33 ± 1.43 mm, and MAPE of 6.29 ± 0.26%. Shape-only and Global-only ablations showed that the spatial pattern and global statistics contributed different information, and the full model outperformed Random Forest, Extra Trees, and template/k-nearest-neighbor baselines on the same test set. The dominant failure mode was azimuth estimation for far-field sources close to the array normal. These results are lim-ited to the simulated test positions and do not establish real-detector performance or reliable full-angle localization at 1 m.