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 in | European physical journal plus Vol. 135; no. 2; p. 140 |
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Main Authors | , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2020
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 2190-5444 2190-5444 |
DOI: | 10.1140/epjp/s13360-020-00122-3 |