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” × 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-ofthe- art energy resolution (2.6% at 662 keV) 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 μs per event, compatible with high gamma-ray counting rate (100 kcps) 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 3 kg weight), showing potential for identification of sources in the field.

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

Buonanno L.;Di Vita D.;Carminati M.;Fiorini C.
2020

Abstract

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” × 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-ofthe- art energy resolution (2.6% at 662 keV) 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 μs per event, compatible with high gamma-ray counting rate (100 kcps) 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 3 kg weight), showing potential for identification of sources in the field.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1149965
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