| Autor: |
Dell'Aquila, Daniele, Gnoffo, Brunilde, Lombardo, Ivano, Porto, Francesco, Russo, Marco |
| Předmět: |
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| Zdroj: |
Journal of Physics G: Nuclear & Particle Physics; Jan2023, Vol. 50 Issue 1, p1-19, 19p |
| Abstrakt: |
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 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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