Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks

Ido, A. Sharghi, Bonyadi, M. R., Etaati, G. R. and Shahriari, M. (2009) Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks. Applied Radiation and Isotopes, 67 10: 1912-1918. doi:10.1016/j.apradiso.2009.05.020

Author Ido, A. Sharghi
Bonyadi, M. R.
Etaati, G. R.
Shahriari, M.
Title Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks
Journal name Applied Radiation and Isotopes   Check publisher's open access policy
ISSN 0969-8043
Publication date 2009-10-01
Year available 2009
Sub-type Article (original research)
DOI 10.1016/j.apradiso.2009.05.020
Open Access Status Not yet assessed
Volume 67
Issue 10
Start page 1912
End page 1918
Total pages 7
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Language eng
Formatted abstract
Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both 241Am-Be and 252Cf neutron sources. The results of neural network are in good agreement with FORIST code. Crown Copyright
Keyword Artificial neural network
Neutron spectrum unfolding
Scintillation detector
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Centre for Advanced Imaging Publications
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