Robust extrapolation using physics-related activation functions in neural networks for nuclear masses

TL;DR

This paper introduces physics-related activation functions in neural networks to improve the extrapolation of nuclear mass predictions, achieving high accuracy without relying on existing global models or magic number knowledge.

Contribution

The study demonstrates that replacing nonlinear functions with physics-related ones enhances neural network extrapolation in nuclear mass predictions, providing better understanding and accuracy.

Findings

Significant improvement in extrapolation performance.

Effective prediction of light nuclei up to drip lines.

Model trained only on inner region data.

Abstract

Given the importance of nuclear mass predictions, numerous models have been developed to extrapolate the measured data into unknown regions. While neural networks -- the core of modern artificial intelligence -- have been recently suggested as powerful methods, showcasing high predictive power in the measured region, their ability to extrapolate remains questionable. This limitation stems from their `black box' nature and large number of parameters entangled with nonlinear functions designed in the context of computer science. In this study, we demonstrate that replacing such nonlinear functions with physics-related functions significantly improves extrapolation performance and provides enhanced understanding of the model mechanism. Using only the information about neutron (N) and proton (Z) numbers without any existing global mass models or knowledge of magic numbers, we developed a highly accurate model that covers light nuclei (N, Z > 0) up to the drip lines. The extrapolation performance was rigorously evaluated using the outermost nuclei in the measurement landscape, and only the data in the inner region was used for training. We present details of the method and model, along with opportunities for future improvements.