Statistical modeling of nuclear properties and merging of single-particle levels in finite Fermi systems.

摘要

In Part I we have made initial studies of the potential of support vector machines (SVM) for providing statistical models of nuclear systematics with demonstrable predictive power. Using SVM regression and classification procedures, we have created global models of atomic masses, beta-decay halflives, and ground-state spins and parities. These models exhibit performance in both data-fitting and prediction that is comparable to that of the best global models from nuclear phenomenology and microscopic theory, as well as the best statistical models based on multilayer feedforward neural networks. In Part II, properties of the distribution of single-particle levels adjacent to the Fermi surface in finite Fermi systems are studied, focusing on the case in which these levels are degenerate. The interaction of the quasiparticles occupying these levels lifts the degeneracy and affects the distance between the closest levels on opposite sides of the Fermi surface, as the number of particles in the system is varied. In addition to the familiar scenario of level crossing, a new phenomenon is uncovered, in which the merging of single-particle levels results in the disappearance of well-defined single-particle excitations. Implications of this finding are discussed for nuclear, solid-state, and atomic systems.