Two analysis methods for Zeff evaluation were explored using both experimental and simulated gamma-ray attenuation data. Using particle-capture reactions on composite targets to generate multi-monoenergetic gamma rays between 1 and 12 MeV, we demonstrate the advantage of using neural networks for effective Z evaluation of shielded materials in single-pixel measurements. Furthermore, we extend the analysis to 2D processing of transmission radiography and by using Geant4-simulated data we prove the superiority of artificial neural networks in terms of image quality and material discrimination against classical methods.

Effective Z evaluation using monoenergetic gamma rays and neural networks

Guardo G. L.;Lattuada D.;
2020

Abstract

Two analysis methods for Zeff evaluation were explored using both experimental and simulated gamma-ray attenuation data. Using particle-capture reactions on composite targets to generate multi-monoenergetic gamma rays between 1 and 12 MeV, we demonstrate the advantage of using neural networks for effective Z evaluation of shielded materials in single-pixel measurements. Furthermore, we extend the analysis to 2D processing of transmission radiography and by using Geant4-simulated data we prove the superiority of artificial neural networks in terms of image quality and material discrimination against classical methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/521956
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