Comparative study of neural network and model averaging methods in nuclear β-decay half-life predictions

• Published in: Journal of physics. G, Nuclear and particle physics Vol. 51; no. 1; pp. 15103 - 15116
• Main Authors: Li, W F, Zhang, X Y, Niu, Y F, Niu, Z M
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01.01.2024
• Subjects: decay half-life; model averaging method; neural network; nuclear
• ISSN: 0954-3899; 1361-6471
• DOI: 10.1088/1361-6471/ad0314

Abstract Nuclear β -decay half-lives are investigated using the two-hidden-layer neural network and compared with the model averaging method. By carefully designing the input and hidden layers of the neural network, the neural network achieves better accuracy of nuclear β -decay half-life predictions and well eliminates the too strong odd–even staggering predicted by the previous neural networks. For nuclei with half-lives less than 1 s, the neural network can describe experimental half-lives within 1.6 times. The half-life predictions of the neural network are further tested with the newly measured half-lives, demonstrating its reliable extrapolation ability not far from the training region. Compared to the model averaging method, the neural network has higher accuracy and smaller uncertainties of half-life predictions in the known region. When extrapolated to the unknown region, the half-life uncertainties of the neural network are still smaller than those of the model averaging method within about 5–10 steps for nuclei with 35 ≲ Z ≲ 90, while the model averaging method has smaller half-life uncertainties for nuclei near the drip line.