Understanding heavy-ion fusion cross section data using novel artificial intelligence approaches
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- @Article{Dell'Aquila:2024:EPJCONF,
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author = "Daniele Dell'Aquila and Brunilde Gnoffo and
Ivano Lombardo and Luigi Redigolo and Francesco Porto and
Marco Russo",
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title = "Understanding heavy-ion fusion cross section data
using novel artificial intelligence approaches",
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journal = "EPJ Web of Conferences",
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year = "2024",
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volume = "292",
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note = "16th Varenna Conference on Nuclear Reaction Mechanisms
(NRM2023)",
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keywords = "genetic algorithms, genetic programming, Heavy-Ion
Reactions",
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conf_address = "Varenna, Italy",
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conf_month = "11-16 June",
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ISSN = "2100-014X",
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URL = "https://inspirehep.net/literature/2781521",
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DOI = "doi:10.1051/epjconf/202429205005",
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size = "7 pages",
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abstract = "We modelled an unprecedentedly large dataset of
complete fusion cross section data using a novel
artificial intelligence approach. Our analysis aims
especially to unveil, in a data-driven way, nuclear
structure effects on the fusion between heavy ions and
to suggest a universal formula capable to describe all
previously available data. The study focused on
light-to-medium-mass nuclei, where incomplete fusion
phenomena are more difficult to occur and less likely
to contaminate the data. The method used to derive the
models exploits a state-of-the-art hybridization of
genetic programming and artificial neural networks and
is capable to derive an analytical expression that
serves to predict integrated cross section values. For
the first time, we analysed a comprehensive set of
nuclear variables, including quantities related to the
nuclear structure of projectile and target. we describe
the derivation of two computationally simple models
that can satisfactorily describe, with a reduced number
of variables and only a few parameters, a large variety
of light-to-intermediate-mass collision systems in an
energy domain ranging approximately from the Coulomb
barrier to the oncet of multi-fragmentation phenomena.
The underlying methods are particularly innovative and
are of potential use for a broad domain of applications
in the nuclear field.",
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notes = "See \cite{Dell_Aquila:2023:JPCS}
Dipartimento di Fisica {"}Ettore Pancini{"}, University
of Naples {"}Federico II{"}, Napoli, Italy",
- }
Genetic Programming entries for
Daniele Dell'Aquila
Brunilde Gnoffo
Ivano Lombardo
Luigi Redigolo
Francesco Porto
Marco Russo
Citations