This work presents the experimental results for the position estimation of the interaction point of gamma photons in a thick 3”3” cylindrical scintillation crystal, when implementing machine learning algorithms. For the experimental characterization, a LaBr3 crystal is coupled to an array of 144 SiPM detectors. The machine learning algorithms implemented are: k-NN, Decision Tree, and Support Vector Machine. Principal Components Analysis is also implemented; this algorithm enhances the classification accuracy. Experimental results show a planar resolution close to 1 cm, suggesting the adoption of these algorithms and electronics architecture in nuclear physics experiments to correct the Doppler broadening effect. The algorithms can be executed both in post-processing and in real-time. Edge computing of coordinates and energy, on microcontroller or FPGA devices with sub-μs processing time per scintillation event, is in this context successfully applied, leading to a relaxation of data transmission rate constraints, and to the achievement of real-time position sensitivity of gamma photons.

Implementation of Real-Time Machine Learning Algorithms for 3D Scintillation Position Estimation in Thick Crystals

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

Abstract

This work presents the experimental results for the position estimation of the interaction point of gamma photons in a thick 3”3” cylindrical scintillation crystal, when implementing machine learning algorithms. For the experimental characterization, a LaBr3 crystal is coupled to an array of 144 SiPM detectors. The machine learning algorithms implemented are: k-NN, Decision Tree, and Support Vector Machine. Principal Components Analysis is also implemented; this algorithm enhances the classification accuracy. Experimental results show a planar resolution close to 1 cm, suggesting the adoption of these algorithms and electronics architecture in nuclear physics experiments to correct the Doppler broadening effect. The algorithms can be executed both in post-processing and in real-time. Edge computing of coordinates and energy, on microcontroller or FPGA devices with sub-μs processing time per scintillation event, is in this context successfully applied, leading to a relaxation of data transmission rate constraints, and to the achievement of real-time position sensitivity of gamma photons.
IEEE NSS/MIC 2020 Conference Records
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1170741
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