Effective Z evaluation using monoenergetic gamma rays and neural networks

Two analysis methods for Z 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 e...

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Published inEuropean physical journal plus Vol. 135; no. 2; p. 140
Main Authors Turturica, G. V., Iancu, V., Pappalardo, A., Söderström, P.-A., Açıksöz, E., Balabanski, D. L., Capponi, L., Constantin, P., Fugaru, V., Guardo, G. L., Ilie, M., Ilie, S., Iovea, M., Lattuada, D., Nichita, D., Petruse, T., Spataru, A., Ur, C. A.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2020
Springer Nature B.V
Subjects
Applied and Technical Physics
Artificial neural networks
Atomic
Complex Systems
Condensed Matter Physics
Data analysis
Electrons
Energy
Gamma rays
Image quality
Mathematical and Computational Physics
Methods
Molecular
Neural networks
Optical and Plasma Physics
Optimization
Physics
Physics and Astronomy
Radiography
Regular Article
Theoretical
Two dimensional analysis
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Summary:Two analysis methods for Z 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.
ISSN:2190-5444
2190-5444
DOI:10.1140/epjp/s13360-020-00122-3