Statistical learnability of nuclear masses

Abstract

After more than 80 years from the seminal work of Weizsäcker and the liquid drop model of the atomic nucleus, deviations from experiments of mass models ($\sim$ MeV) are orders of magnitude larger than experimental errors ($\lesssim$ keV). Predicting the mass of atomic nuclei with precision is extremely challenging. This is due to the non--trivial many--body interplay of protons and neutrons in nuclei, and the complex nature of the nuclear strong force. Statistical theory of learning will be used to provide bounds to the prediction errors of model trained with a finite data set. These bounds are validated with neural network calculations, and compared with state of the art mass models. Therefore, it will be argued that the nuclear structure models investigating ground state properties explore a system on the limit of the knowledgeable, as defined by the statistical theory of learning.

References in corpus (6)

• Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
• Refining mass formulas for astrophysical applications: a Bayesian neural network approach
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Cited by in corpus (4)

• Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron
• Principal components of nuclear mass models
• Application of multilayer perceptron with data augmentation in nuclear physics
• Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach