NEUTRON SPECTRUM UNFOLDING USING VARIOUS COMPUTER CODES: A COMPARISON

Dosimetric quantities at various distances from a 30 cm diameter polyethylene sphere moderated 241Am-Be source were investigated using the Bonner sphere spectrometer system. The different international commercial unfolding codes were applied to unfold the neutron spectrum, and their shapes were compared to each other. Additionally, the integrated neutron fluence rates overall spectrum and fractional neutron fluence rates were deduced and compared between the results obtained from different unfolding codes. As an important quantity applying in radiation safety assessment, the neutron ambient dose equivalent rates were also calculated and compared to each other to verify the utility feasibilities of the codes.

EVALUATION OF ARTIFICIAL NEURAL NETWORKS EFFECTIVENESS FOR UNFOLDING GAMMA-SPECTRUM OF 137CS

Development of machine learning methods for spectrum processing is one of the most promising ways for gamma- spectrometry automation and accuracy improvement. Effectiveness of fully connected and convolution neural networks for quantitative γ-spectrometry analysis using scintillation detector NaI(Tl) and lead shielding is presented in the article. Semi-synthetic spectrums were used for the models training; the semi-synthetic spectrums are in channels additions of random spectrums measured at a short duration. The analysis shows advantages of artificial neural networks compare to the common analytical method of spectrum unfolding. The mean square error of activity evaluation is 2–4 times lower than the common method if measuring time is equal to 100 s. In highly standardized conditions of measuring, the advantages of convolution neural networks appear with increasing radiation source activity. Validation with sources not used in training of neural networks has shown fully connected and convolution neural networks can have advantages over the standard method when activity of γ-radiation source is relatively high.