Predicting nuclear masses with product-unit networks

Accurate estimation of nuclear masses and their prediction beyond the experimentally explored domains of the nuclear landscape are crucial to an understanding of the fundamental origin of nuclear properties and to many applications of nuclear science, most notably in quantifying the r-process of stellar nucleosynthesis. Neural networks have been applied with some success to the prediction of nuclear masses, but they are known to have shortcomings in application to extrapolation tasks. In this work, we propose and explore a novel type of neural network for mass prediction in which the usual neuron-like processing units are replaced by complex-valued product units that permit multiplicative couplings of inputs to be learned from the input data. This generalized network model is tested on both interpolation and extrapolation data sets drawn from the Atomic Mass Evaluation. Its performance is compared with that of common neural-network architectures, substantiating its suitability for nuclear-mass prediction. Additionally, a prediction-uncertainty measure for such complex-valued networks is proposed that allows identifying nuclides of expected large prediction error. Within and beyond the experimentally explored domain, model predictions are evaluated by computing deviations from the Garvey-Kelson relations.