A new method to improve the generalization ability of neural networks: A case study of nuclear mass training

Abstract

As one of the fundamental input quantities in nuclear physics, an accurate calculation of the nucleus mass is necessary. Currently, neural network methods improve the accuracy of mass models. However, the reliability for extrapolation cannot be predicted. In this paper, a training method of neural network method is proposed. A fitting method similar to the macroscopic microscopic mass model is used. After fitting the liquid drop model the neural network method is used for training, and then afterwards the parameters are refitted and the neural network model is retrained, and so on several times. Taking the Weizsäcker-Skyrme (WS) model as an example, for known nuclei, it can improve the calculation results by about 20% over the original results. The extrapolation results for unknown neutron-deficient nuclei are also significantly improved. We believe that this method can calculate the masses of partially unknown nuclei more accurately. Further, it may be possible to improve the extrapolation capability of the neural network method using this approach. It is hoped that the present work can be useful for nuclear physics experiments and applications of neural network methods.