An artificial neural network application on nuclear charge radii

• Published in: Journal of physics. G, Nuclear and particle physics Vol. 40; no. 5; pp. 55106 - 7
• Main Authors: Akkoyun, S, Bayram, T, Kara, S O, Sinan, A
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.05.2013
• Subjects: artificial neural network; Artificial neural networks; Charge; Energy (nuclear); Hartree-Fock-Bogoliubov method; Learning theory; Mathematical models; Neural networks; nuclear charge radii; Nuclear physics; Particle physics
• ISSN: 0954-3899; 1361-6471
• DOI: 10.1088/0954-3899/40/5/055106

Artificial neural networks (ANN) have emerged with successful applications in nuclear physics as well as in many fields of science in recent years. In this paper, ANN have been employed on experimental nuclear charge radii. Statistical modeling of nuclear charge radii using ANN are seen to be successful. Based on the outputs of ANN we have estimated a new simple mass-dependent nuclear charge radii formula. Also, the charge radii, binding energies and two-neutron separation energies of Sn isotopes have been calculated by implementation of a new estimated formula in Hartree-Fock-Bogoliubov calculations. The results of the study show that the new estimated formula is useful for describing nuclear charge radii.