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.

Nuclear charge radii: density functional theory meets Bayesian neural networks

“…Artificial neural networks have also been used in these studies to reproduce, among other things, (a) the differences between experimental nuclear masses and theoretical predictions provided by the Finite Range Droplet Models (FRDM) and (b) β-decay rates of relevance to r-process nucleosynthesis. More recently, artificial neural networks have been used in the study of binding-energy systematics [41] and nuclear charge radii [42].…”

Extrapolation of nuclear structure observables with artificial neural networks

“…In this work, we want to address this challenge and focus on the network robustness and avoidance of multiple solutions. In recent years, artificial neural networks have been used for various extrapolations in nuclear physics [27][28][29][30][31][32][33][34][35], and for the solution of the quantum many-body system [36]. Artificial neural networks use sets of nonlinear functions to describe the complex relationships between input and output variables.…”

A study on estimation of electric quadrupole transition probability in nuclei

“…There are also several theoretical model for prediction of the B(E2)↑ values based on single-shell asymptotic Nilsson model [2], finite-range droplet model [3], Woods-Saxon model [4], relativistic mean-field model [5], extended Thomas-Fermi StrutinskyIntegral method [6], Hartree-Fock+BCS method [7] and dynamical microscopic model [8] Recently, artificial neural network (ANN) has been used in many fields in nuclear physics such as developing nuclear mass systematic [9], identification of impact parameter in heavy-ion 8 collisions [10][11][12], estimating beta decay half-lives [13], neutron-gamma separation in order to obtain clear gamma-ray spectra [14], prediction of peak-tobackground ratio in gamma-ray spectroscopy [15] and obtaining nuclear charge radii [16]. In this study, feed-forward ANN has been used to estimate B(E2)↑ values for some even-even nuclei between 110 ≤ A ≤ 190.…”