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Register a new userAn artificial neural network application on nuclear charge radii
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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.
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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 the presence of nuclear surface. In this work, by studying the dependence of charge and neutron radii, and neutron skin, on nuclear matter parameters, we assess different mechanisms that drive nuclear sizes. We apply nuclear density functional theory using a family of Skyrme functionals obtained by means of different optimization protocols targeting specific nuclear properties. By performing the Monte-Carlo sampling of reasonable functionals around the optimal parametrization, we study correlations between nuclear matter paramaters and observables characterizing charge and neutron distributions. We demonstrate the existence of the strong converse relation between the nuclear charge radii and the saturation density of symmetric nuclear matter and also between the neutron skins and the slope of the symmetry energy. For functionals optimized to experimental binding energies only, proton and neutron radii are weakly correlated due to canceling trends from different nuclear matter parameters. We show that by requiring that the nuclear functional reproduces the empirical saturation point of symmetric nuclear matter practically fixes the charge (or proton) radii, and vice versa. The neutron skin uncertainty primarily depends on the slope of the symmetry energy. Consequently, imposing a constraint on both $rho_0$ and $L$ practically determines the nuclear size, modulo small variations due to shell effects.
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 short-range correlations (np-SRCs) can describe the discontinuity properties and odd-even staggering (OES) effect of charge radii along isotopic chains remarkably well. In this work, we further review the modified rms charge radii formula in the framework of relativistic mean field (RMF) theory. The charge radii are calculated along various isotopic chains that include the nuclei featuring the $N=50$ and $82$ magic shells. Our results suggest that RMF with and without considering correction term give almost similar trend of nuclear size for some isotopic chains with open proton shell, especially the shrink phenomena of charge radii at strong neutron closed shells and the OES behaviors. This suggests that the np-SRCs has almost no influence for some nuclei due to the strong coupling between different levels around Fermi surface. The weakening OES behavior of nuclear charge radii is observed generally at completely filled neutron shells and this may be proposed as a signature of magic indicator.
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 artificial neural networks for observables such as the ground-state energy and the point-proton radius. We extrapolate results from no-core shell model and coupled-cluster calculations to very large model spaces and estimate uncertainties. Training the network on different data typically yields extrapolation results that cluster around distinct values. We show that a preprocessing of input data, and the inclusion of correlations among the input data, reduces the problem of multiple solutions and yields more stable extrapolated results and consistent uncertainty estimates. We perform extrapolations for ground-state energies and radii in $^{4}$He, $^{6}$Li, and $^{16}$O, and compare the predictions from neural networks with results from infrared extrapolations.
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 bunched-beam collinear laser spectroscopy at the CERN-ISOLDE radioactive ion-beam facility. Mean square charge radii are now known from $^{37}$K to $^{51}$K, covering all $ u f_{7/2}$-shell as well as all $ u p_{3/2}$-shell nuclei. These measurements, in conjunction with those of Ca, Cr, Mn and Fe, provide a first insight into the $Z$ dependence of the evolution of nuclear size above the shell closure at N=28.
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 theory, kinks are universally predicted at the $jj$-closed magic numbers and anti-kinks (textit{i.e.} inverted kinks) are newly predicted at the $ell s$-closed magic numbers, both in the charge radii and in the matter radii along the isotopic and isotonic chains where nuclei stay spherical. These results seem consistent with the kinks of the charge radii observed in Ca, Sn and Pb and the anti-kink in Ca. The kinks and the anti-kinks could be a peculiar indicator for magic numbers, discriminating $jj$-closure and $ell s$-closure.
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