Effective Z evaluation using monoenergetic gamma rays and neural networks

Abstract

Two analysis methods for $$\textit{Z}_\mathrm{eff}$$ 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.