Nuclear mass prediction as an image reconstruction problem: can observed pattern determine mass values?

Abstract

Theoretical prediction of nuclear masses is analyzed as a pattern recognition problem on the N-Z plane. A global pattern is observed by plotting the differences between measured masses and Liquid Drop Model (LDM) predictions. After unfolding the data by removing the smooth LDM mass contributions, the remaining microscopic effects have proved difficult to model, although they display a striking pattern. These deviations carry information related to shell closures, nuclear deformation and the residual nuclear interactions. In the present work the more than 2000 known nuclear masses are studied as an array in the N-Z plane viewed through a mask, behind which the approximately 7000 unknown unstable nuclei that can exist between the proton and neutron drip lines are hidden. We show here that employing a Fourier transform deconvolution method these by masses can be predicted with similar accuracy than standard methods. We believe that a more general approach needs to be implemented, however, to optimize the procedures predictive power. Thus, while we see the need to study and implement alternative image reconstruction and extrapolation methods, the general ideas are already contained in this paper.