Neutron spectra unfolding in Bonner spheres spectrometry using neural networks
| Autor: | R. Koohi-Fayegh, Mohammad Reza Kardan, M. Ghiassi-Nejad, Saeed Setayeshi |
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| Rok vydání: | 2003 |
| Předmět: |
Radiation Dosage
Sensitivity and Specificity Particle detector Radiation Protection Nuclear magnetic resonance Dosimetry Neutron detection Computer Simulation Radiology Nuclear Medicine and imaging Radiometry Neutrons Physics Radiation Radiological and Ultrasound Technology Artificial neural network Spectrometer Equivalent dose Public Health Environmental and Occupational Health Reproducibility of Results General Medicine Models Theoretical Computational physics Spectrometry Gamma Measuring instrument Thermoluminescent Dosimetry SPHERES Neural Networks Computer Algorithms |
| Zdroj: | Scopus-Elsevier |
| ISSN: | 1742-3406 0144-8420 |
| Popis: | The neural network method has been used for the unfolding of neutron spectra in neutron spectrometry by Bonner spheres. A back propagation algorithm was used for training of neural networks. 4 mm x 4 mm bare LiI (Eu) and in a polyethylene sphere set: 2, 3, 4, 5, 6, 7, 8, 10, 12, 18 inch diameter have been used for unfolding of neutron spectra. Neural networks were trained by 199 sets of neutron spectra, which were subdivided into 6, 8, 10, 12, 15 and 20 energy bins and for each of them an appropriate neural network was designed and trained. The validation was performed by the 21 sets of neutron spectra. A neural network with 10 energy bins which had a mean value of error of 6% for dose equivalent estimation of spectra in the validation set showed the best results. The obtained results show that neural networks can be applied as an effective method for unfolding neutron spectra especially when the main target is neutron dosimetry. |
| Databáze: | OpenAIRE |
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