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arXiv · 2203.10367

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

Daniele Dell'Aquila·Brunilde Gnoffo·Ivano Lombardo·Francesco Porto·Marco Russo
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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.

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Daniele Dell'Aquila, Brunilde Gnoffo, Ivano Lombardo, Francesco Porto, Marco Russo. 2022-03-19. Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach. https://doi.org/10.1088/1361-6471%2Fac9ad1

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