用机器学习外推核模型计算结果至无限空间，精度达百千电子伏。
Extrapolation to infinite model space of no-core shell model calculations using machine learning
- 构建神经网络集成模型，外推有限模型空间的核态能量与半径
- 对⁶Li、⁶He等核的基态能精确到数百keV内，半径收敛良好
- 适用于轻核能谱与结构研究，尤其擅长处理未束缚态问题
本文采用神经网络集成方法，将使用Daejeon16核力势在不同模型空间及不同ℏΩ基底参数下得到的无核心壳模型（NCSM）计算结果外推至无限模型空间。我们系统回顾了对轻核束缚态与非束缚态能量及质子、中子、物质分布的均方根半径的外推结果。该方法可获得收敛预测并量化不确定性。⁶Li、⁶He的基态能量以及非束缚态⁶Be的基态能量，均与实验值偏差在数百keV以内；⁶Li的激发态(3⁺,0)和(0⁺,1)能量也高度吻合。束缚态的半径外推结果稳定收敛；而⁶Be和⁶Li的非束缚态半径则未呈现稳定趋势。
原文摘要 · Abstract (English)
An ensemble of neural networks is employed to extrapolate no-core shell model (NCSM) results to infinite model space for light nuclei. We present a review of our neural network extrapolations of the NCSM results obtained with the Daejeon16 NN interaction in different model spaces and with different values of the NCSM basis parameter $\hbarΩ$ for energies of nuclear states and root-mean-square (rms) radii of proton, neutron and matter distributions in light nuclei. The method yields convergent predictions with quantifiable uncertainties. Ground-state energies for $^{6}$Li, $^{6}$He, and the unbound $^{6}$Be, as well as the excited $(3^{+},0)$ and $(0^{+},1)$ states of $^{6}$Li, are obtained within a few hundred keV of experiment. The extrapolated radii of bound states converge well. In contrast, radii of unbound states in $^{6}$Be and $^{6}$Li do not stabilize.
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