Garvey-Kelson relations for nuclear charge radii

The Garvey-Kelson relations (GKRs) are algebraic expressions originally developed to predict nuclear masses. In this letter we show that the GKRs provide a fruitful framework for the prediction of other physical observables that also display a slowly-varying dynamics. Based on this concept, we extend the GKRs to the study of nuclear charge radii. The GKRs are tested on 455 out of the approximately 800 nuclei whose charge radius is experimentally known. We find a rms deviation between the GK predictions and the experimental values of only 0.01 fm. This should be contrasted against some of the most successful microscopic models that yield rms deviations almost three times as large. Predictions -with reliable uncertainties- are provided for 116 nuclei whose charge radius is presently unknown.

Study of spin polarized nuclear matter and finite nuclei with finite range simple effective interaction

The properties of spin polarized pure neutron matter and symmetric nuclear matter are studied using the finite range simple effective interaction, upon its parametrization revisited. Out of the total twelve parameters involved, we now determine ten of them from nuclear matter, against the nine parameters in our earlier calculation, as required in order to have predictions in both spin polarized nuclear matter and finite nuclei in unique manner being free from uncertainty found using the earlier parametrization. The information on the effective mass splitting in polarized neutron matter of the microscopic calculations is used to constrain the one more parameter, that was earlier determined from finite nucleus, and in doing so the quality of the description of finite nuclei is not compromised. The interaction with the new set of parameters is used to study the possibilities of ferromagnetic and antiferromagnetic transitions in completely polarized symmetric nuclear matter. Emphasis is given to analyze the results analytically, as far as possible, to elucidate the role of the interaction parameters involved in the predictions.