The use of artificial neural networks for the unfolding procedures in neutron activation measurements

Abstract

The MAXED and GRAVEL unfolding algorithms have been used to determine cross-sections, with the NAXSUN method developed at JRC-Geel. This study explores the potential of a particular type of artificial neural network, the multilayer perceptron (MLP), as an alternative to traditional unfolding algorithms. By generating a training dataset using the TALYS 2.0 code and testing the MLP model on real experimental data, we compared the effectiveness of MLP in unfolding neutron-induced reactions cross sections involving indium and rhenium isotopes. The results were benchmarked against those obtained using standard unfolding algorithms and TALYS 2.0 simulations, demonstrating the advantages and limitations of the ANN approach. The obtained results show a much-reduced corridor of uncertainty in the derived cross-section curves compared to previous work using traditional unfolding techniques.