Statistical modeling with neural nets: nuclear masses and halflives

Abstract

Multilayer feedforward neural networks are used to create global models of atomic masses and lifetimes of nuclear states, with the goal of effective prediction of the properties of nuclides outside the region of stability. Innovations in coding and training schemes are used to improve the extrapolation capability of models of the mass table. Studies of nuclear lifetimes have focused on ground states that decay 100% via the β- mode. Results are described which demonstrate that in predictive acuity, statistical approaches to global modeling based on neural networks are potentially competitive with the best phenomenological models based on the traditional methods of theoretical physics.