A compact module based on arrays of solid-state SiPM detectors (up to 144), integrated front-end and a crystal with large scintillation efficiency (3' LaBr3:Ce) for detection of gamma rays is presented. In addition to state-of-the-art energy resolution (2.6% at 662 keV) and sub-centimeter spatial resolution in reconstruction of the photon interaction point, this portable unit enables the angular localization of gamma sources as a function of the reconstructed interaction point distribution. The latter task is performed by means of k-NN and decision tree classifiers. The implementation of the reconstruction algorithm in the micro-controller of the acquisition node allows for relaxation of constraints in data transmission and, in case of cooperative networks, distribution of computational complexity. The system architecture and design choices are here presented in detail, followed by preliminary experimental results obtained with a 16 channel front-end ASIC.

A SiPM-Based Directional Gamma-Ray Spectrometer with Embedded Machine Learning

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

Abstract

A compact module based on arrays of solid-state SiPM detectors (up to 144), integrated front-end and a crystal with large scintillation efficiency (3' LaBr3:Ce) for detection of gamma rays is presented. In addition to state-of-the-art energy resolution (2.6% at 662 keV) and sub-centimeter spatial resolution in reconstruction of the photon interaction point, this portable unit enables the angular localization of gamma sources as a function of the reconstructed interaction point distribution. The latter task is performed by means of k-NN and decision tree classifiers. The implementation of the reconstruction algorithm in the micro-controller of the acquisition node allows for relaxation of constraints in data transmission and, in case of cooperative networks, distribution of computational complexity. The system architecture and design choices are here presented in detail, followed by preliminary experimental results obtained with a 16 channel front-end ASIC.
Proceedings - 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2020
9781728149226
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1146048
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