Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach
Abstract
We perform a comprehensive analysis of complete fusion cross section data with the aim to derive, in a completely data-driven way, a model suitable to predict the integrated cross section of the fusion between light to medium mass nuclei at above barrier energies. To this end, we adopted a novel artificial intelligence approach, based on a hybridization of genetic programming and artificial neural networks, capable to derive an analytical model for the description of experimental data. The approach enables, for the first time, to perform a global search for computationally simple models over several variables and a considerable body of nuclear data. The derived phenomenological formula can serve to reproduce the trend of fusion cross section for a large variety of light to intermediate mass collision systems in an energy domain ranging approximately from the Coulomb barrier to the onset of multi-fragmentation phenomena.
Keywords
Cite
@article{arxiv.2203.10367,
title = {Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach},
author = {Daniele Dell'Aquila and Brunilde Gnoffo and Ivano Lombardo and Francesco Porto and Marco Russo},
journal= {arXiv preprint arXiv:2203.10367},
year = {2022}
}