Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network

Abstract

An artificial neural network (ANN) model was used for the prediction of peak-to-background ratio (PBR) as a function of measurement time in gamma-ray spectrometry. In order to make the ANN model with good predictive power, the ANN parameters were optimized simultaneously employing a variable-size simplex method. Most of the predicted and the experimental PBR values for eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137, and Be-7) commonly detected in soil samples agreed to within +/- 19.4% of the expanded uncertainty and 2.61% of average bias.