An artificial neural network application on nuclear charge radii

Abstract

The artificial neural networks (ANNs) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, by using (ANNs), we have constructed a formula for the nuclear charge radii. Statistical modeling of nuclear charge radii by using ANNs has been seen as to be successful. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementing of the new formula in Hartree-Fock-Bogoliubov (HFB) calculations. The results of the study shows that the new formula is useful for describing nuclear charge radii.

7 pages, 3 figures

References in corpus (3)

• Axially Deformed Solution of the Skyrme-Hartree-Fock-Bogolyubov Equations using The Transformed Harmonic Oscillator Basis. The program HFBTHO (v1.66p)
• Nuclear mass systematics using neural networks
• Neural Networks for Impact Parameter Determination

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