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Your search keyword '"Artificial intelligence in nuclear data"' showing total 2 results
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2 results on '"Artificial intelligence in nuclear data"'

1. Modeling heavy-ion fusion cross section data via a novel artificial intelligence approach.

Author

Dell'Aquila, Daniele, Gnoffo, Brunilde, Lombardo, Ivano, Porto, Francesco, and Russo, Marco

Subjects
*ARTIFICIAL intelligence, *COULOMB barriers (Nuclear fusion), *ACTIVATION energy
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 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]

Published
2023
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2. Automatic classification of nuclear physics data via a Constrained Evolutionary Clustering approach

Author

Daniele Dell'Aquila and Marco Russo

Subjects
Online and offline, Clustering algorithms, Data classification, FOS: Physical sciences, General Physics and Astronomy, Charged particle identification in nuclear collisions, Evolutionary computing, 01 natural sciences, Evolutionary computation, 010305 fluids & plasmas, Artificial intelligence in nuclear data, classification of data in nucleus-nucleus collisions, genetic programming, artificial neural networks, Nuclear physics, 0103 physical sciences, Nuclear Experiment (nucl-ex), Nuclear physics data classification, 010306 general physics, Cluster analysis, Nuclear Experiment, Detector, Vector quantization, Experimental data, Computational Physics (physics.comp-ph), Hardware and Architecture, Physics - Data Analysis, Statistics and Probability, Noise (video), Physics - Computational Physics, Data Analysis, Statistics and Probability (physics.data-an)
Abstract

This paper presents an automatic method for data classification in nuclear physics experiments based on evolutionary computing and vector quantization. The major novelties of our approach are the fully automatic mechanism and the use of analytical models to provide physics constraints, yielding to a fast and physically reliable classification with nearly-zero human supervision. Our method is successfully validated using experimental data produced by stacks of semiconducting detectors. The resulting classification is highly satisfactory for all explored cases and is particularly robust to noise. The algorithm is suitable to be integrated in the online and offline analysis software of existing large complexity detection arrays for the study of nucleus–nucleus collisions at low and intermediate energies.

Published
2020

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