Studies of different kernel functions in nuclear mass predictions with kernel ridge regression

Abstract

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The kernel ridge regression (KRR) approach has been successfully applied in nuclear mass predictions. Kernel function plays an important role in the KRR approach. In this work, the performances of different kernel functions in nuclear mass predictions are carefully explored. The performances are illustrated by comparing the accuracies of describing experimentally known nuclei and the extrapolation abilities. It is found that the accuracies of describing experimentally known nuclei in the KRR approaches with most of the adopted kernels can reach the same level around 195 keV, and the performance of the Gaussian kernel is slightly better than other ones in the extrapolation validation for the whole range of the extrapolation distances.

Keywords

• nuclear mass
• machine-learning
• kernel ridge regression
• kernel function
• hyperparameter