Nuclear masses are machine-learned as a function of proton and neutron numbers. The neural network with additive Gaussian process regression-optimized activation functions (GPR-NN) method is employed for the first time for this purpose. GPR-NN combines the advantages of both neural networks and Gaussian process regression, in that it possesses the expressive power of a NN, in principle allowing modeling any kind of dependence of nuclear mass on the features, and robustness of a linear regression against overfitting. A study of the GPR-NN approach for interpolation and extrapolation in nuclear mass predictions is presented. It is found that the optimal hyperparameters for the GPR-NN approach in interpolation and extrapolation are different. If an appropriate set of hyperparameters is adopted, the GPR-NN approach can achieve good extrapolation performance for nuclear mass prediction, which could potentially help improve the mass predictions of a large number of currently experimentally unknown nuclei.

Close

PrevNext

View figure in article

• Binding energy & masses
• Nuclear binding
• Artificial neural networks