Estimation of fission barrier heights for even–even superheavy nuclei using machine learning approaches
Özet
With the fission barrier height information, the survival probabilities of super-
heavy nuclei can also be reached. Therefore, it is important to have accurate
knowledge of fission barriers, for example, the discovery of super-heavy
nuclei in the stability island in the super-heavy nuclei region. In this study, five
machine learning techniques, Cubist model, Random Forest, support vector
regression, extreme gradient boosting and artificial neural network were used
to accurately predict the fission barriers of 330 even–even super-heavy nuclei
in the region 140 N 216 with proton numbers between 92 and 120. The
obtained results were compared both among themselves and with other
theoretical model calculation estimates and experimental results. According to
the results obtained, it was concluded that the Cubist model, support vector
regression and extreme gradient boosting methods generally gave better results
and could be a better tool for estimating fission barrier heights.