A Directional Gamma-Ray Spectrometer With Microcontroller-Embedded Machine Learning

The enhancement of a compact gamma-ray detection module for spectroscopy and imaging with machine learning for directional sensitivity is presented. In particular this development is targeted towards drone-based localization of radioactive sources in the environment. The unit is composed of a cylindrical monolithic scintillator crystal (3”<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 3” LaBr₃(Ce³⁺+Sr²⁺)), read by an array of 144 solid-state SiPM detectors whose signals are conditioned by an integrated front-end. In addition to state-of-the-art energy resolution (2.6% at 662keV) and sub-centimeter spatial resolution in the reconstruction of the photon interaction point projected on the base, this portable unit enables the 2D angular localization of gamma sources on a plane parallel to the detectors array as a function of the reconstructed interaction point distribution, thanks to a decision tree. The classifier is compared with other techniques (k-NN, PCA) and optimized with 1000 splits. It runs in a 32-bit ARM micro-controller for real-time operation with a processing time of 2.75<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> per event, compatible with high gamma-ray counting rate (100kcps) operation. Despite the absence of a collimator, classification is correct within ±30° for a single photon. Angular resolution of 0.5° and accuracy better than 2° are experimentally demonstrated (along with 0.8W power consumption and 3kg weight), showing potential for identification of sources in the field.