Moscato Pablo, Grebogi Rafael
The University of Newcastle, School of Information and Physical Sciences, Callaghan, NSW, 2308, Australia.
Sci Rep. 2024 May 21;14(1):11559. doi: 10.1038/s41598-024-61389-5.
Understanding nuclear behaviour is fundamental in nuclear physics. This paper introduces a data-driven approach, Continued Fraction Regression (cf-r), to analyze nuclear binding energy (B(A, Z)). Using a tailored loss function and analytic continued fractions, our method accurately approximates stable and experimentally confirmed unstable nuclides. We identify the best model for nuclides with , achieving precise predictions with residuals smaller than 0.15 MeV. Our model's extrapolation capabilities are demonstrated as it converges with upper and lower bounds at the nuclear mass limit, reinforcing its accuracy and robustness. The results offer valuable insights into the current limitations of state-of-the-art data-driven approaches in approximating the nuclear binding energy. This work provides an illustration on the use of analytical continued fraction regression for a wide range of other possible applications.
理解原子核行为是核物理学的基础。本文介绍了一种数据驱动的方法——连分数回归（cf-r），用于分析原子核结合能（B(A, Z)）。通过使用定制的损失函数和解析连分数，我们的方法能够准确地逼近稳定的以及经实验证实的不稳定核素。我们确定了适用于特定条件下核素的最佳模型，实现了残差小于0.15 MeV的精确预测。我们的模型在核质量极限处与上下界收敛，展示了其外推能力，进一步增强了其准确性和稳健性。这些结果为当前最先进的数据驱动方法在逼近原子核结合能方面的局限性提供了有价值的见解。这项工作为解析连分数回归在广泛的其他可能应用中的使用提供了一个示例。
