Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach

TL;DR

This paper introduces a novel AI-based hybrid method combining genetic programming and neural networks to model and predict fusion cross sections in nuclear physics, providing a simple analytical formula applicable across various systems and energies.

Contribution

The study presents the first data-driven AI approach that derives an analytical model for nuclear fusion cross sections using a hybrid genetic programming and neural network method.

Findings

Successfully derived a phenomenological formula fitting experimental data

Model accurately predicts fusion cross sections across multiple systems

Approach enables global search for simple models over extensive data

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.