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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.
Radii of charge and neutron distributions are fundamental nuclear properties. They depend on both nuclear interaction parameters related to the equation of state of infinite nuclear matter and on quantal shell effects, which are strongly impacted by
A unified theoretical model reproducing charge radii of known atomic nuclei plays an essential role to make extrapolations in the regions of unknown nuclear size. Recently developed new ansatz which phenomenally takes into account the neutron-proton
Calculations of nuclei are often carried out in finite model spaces. Thus, finite-size corrections enter, and it is necessary to extrapolate the computed observables to infinite model spaces. In this work, we employ extrapolation methods based on art
We report on the measurement of optical isotope shifts for $^{38,39,42,44,46text{-}51}$K relative to $^{47}$K from which changes in the nuclear mean square charge radii across the N=28 shell closure are deduced. The investigation was carried out by b
Influence of magic numbers on nuclear radii is investigated via the Hartree-Fock-Bogolyubov calculations and available experimental data. With the $ell s$ potential including additional density-dependence suggested from the chiral effective field the