Estimation of (n,p) reaction cross sections at 14.5 ∓ 0.5 MeV neutron energy by using artificial neural network

Abstract

The aim of this study is to develop an accurate artificial neural network algorithm for the cross-section of (n,p) reactions at 14.5 ∓0.5 MeV neutron energy which is important to developing materials for fusion reactor design. The experimental data used at artificial Neural network calculations have been taken from the Experimental Nuclear Reaction Data (EXFOR) database. Bayesian algorithm has been used at training section of artificial neural network. Regression (R) values of artificial neural network calculations have been found as 0.99363, 0.98574 and 0.99257 for training, testing and all process respectively. In addition to artificial neural network calculations, TALYS 1.95 nuclear reaction code has been used to reproduce (n,p) reactions at 14.5 ∓0.5 MeV. Two-component exciton model and Constant Temperature Fermi Gas Model have been used as pre-equilibrium and level density models respectively. Mean square errors of our calculations have been found 48.51 and 495.06 for artificial neural network and TALYS 1.95 respectively. Artificial Neural network estimations have been compared and analyzed with the TALYS 1.95 calculations and the experimental data taken from EXFOR database.