https://doi.org/10.1140/epjp/s13360-020-00122-3
Regular Article
Effective Z evaluation using monoenergetic gamma rays and neural networks
1
ELI-NP, Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering, 077125, Bucharest-Magurele, Romania
2
Politehnica University of Bucharest, 060042, Bucharest, Romania
3
DRMR, Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering, 077125, Bucharest-Magurele, Romania
4
DAT, Horia Hulubei National Institute for R&D in Physics and Nuclear Engineering, 077125, Bucharest-Magurele, Romania
5
Accent Pro 2000, 077125, Bucharest-Magurele, Romania
* e-mail: violeta.iancu@eli-np.ro
Received:
3
September
2019
Accepted:
3
January
2020
Published online:
28
January
2020
Two analysis methods for 
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.
© Società Italiana di Fisica (SIF) and Springer-Verlag GmbH Germany, part of Springer Nature, 2020
