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A Directional Gamma-Ray Spectrometer With Microcontroller-Embedded Machine Learning
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DOI：10.1109/JETCAS.2020.3029570.png)
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Abstract
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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 '' x 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 mu s per event, compatible with high gamma-ray counting rate (100kcps) operation. Despite the absence of a collimator, classification is correct within +/- 30 degrees for a single photon. Angular resolution of 0.5 degrees and accuracy better than 2 degrees are experimentally demonstrated (along with 0.8W power consumption and 3kg weight), showing potential for identification of sources in the field.
Keywords:
Decision trees
Microcontrollers
Silicon photonics
Gamma-ray detection
Edge computing
Machine learning
Gamma spectroscopy
machine learning
imaging
SiPM
edge-computing
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