Estimation of fusion-evaporation cross sections by artificial neural networks

TL;DR

This paper demonstrates that artificial neural networks can effectively estimate fusion-evaporation cross sections, achieving lower deviations than traditional theoretical models, thus aiding nuclear physics research.

Contribution

The study introduces a neural network approach for predicting fusion-evaporation cross sections, outperforming common theoretical calculations in accuracy.

Findings

Root mean square errors of 18.5 and 110.4 mb for training and testing.

Deviations of 1.8% and 10.5% from experimental data.

ANN method shows high potential for nuclear reaction cross section estimation.

Abstract

Accurate determination of fusion-evaporation reaction cross section is important in experimental nuclear physics studies. In this study, by using artificial neural network (ANN) method, we have estimated the cross section values for different reactions. The related root mean square errors have been obtained as 18.5 and 110.4mb for training and test data which correspond to 1.8% and 10.5% deviations from the experimental values, respectively.This order of deviations is lower than the cross section value from most common theoretical calculations. The results of this study indicate that ANN method is capable for the estimation of cross section values of fusion-evaporation reactions.