Combined application of Monte Carlo method and neural networks to simulate qualitative prompt gamma neutron activation analysis

Combined application of Monte Carlo method and neural networks to simulate qualitative prompt gamma neutron activation analysis

Prompt gamma spectrum produced by thermal neutron absorption and fast neutron inelastic scattering is simulated using Monte Carlo code MCNP4C. The simulated spectrum is analyzed with artificial neural network techniques. The neural network in our study is trained based on back-propagation algorithm...

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Veröffentlicht in:Journal of radioanalytical and nuclear chemistry 2010-02, Vol.283 (2), p.403-407
Hauptverfasser: Doostmohammadi, V., Sardari, D., Nasrabadi, A. M.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Chemistry
Chemistry and Materials Science
Computer simulation
Diagnostic Radiology
Fast neutrons
Gamma ray spectra
Gamma rays
Hadrons
Heavy Ions
Inelastic scattering
Inorganic Chemistry
Learning theory
Methods
Monte Carlo method
Monte Carlo methods
Neural networks
Nuclear Chemistry
Nuclear Physics
Physical Chemistry
Technology application
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Zusammenfassung:Prompt gamma spectrum produced by thermal neutron absorption and fast neutron inelastic scattering is simulated using Monte Carlo code MCNP4C. The simulated spectrum is analyzed with artificial neural network techniques. The neural network in our study is trained based on back-propagation algorithm with 138 gamma ray spectra. Elements existing in the 20 different substances are specified. The ANN could identify elements correctly in 96% of input cases.
ISSN:0236-5731
1588-2780
DOI:10.1007/s10967-009-0413-z