发表机构
The University of Osaka; RIKEN Nishina Center for Accelerator-Based Science; Center for Computational Sciences, University of Tsukuba（大阪大学; 理化学研究所仁科加速器科学研究中心; 筑波大学计算科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用从头计算得到的有限核密度分布，通过贝叶斯分析约束Skyrme能量密度泛函参数，并成功预测了未参与训练的208Pb性质，展示了该框架的预测能力。
AI 中文摘要
在核多体物理学中，能量密度泛函（EDF）理论是描述有限核和核物质最强大的方法之一。然而，其预测能力取决于将模型参数校准到实验和观测数据。在本工作中，我们研究了一种替代方法：利用从从头计算（ab initio）获得的有限核连续密度分布来约束参数。我们应用贝叶斯分析，从16O、40Ca和48Ca的密度分布和结合能推断Skyrme EDF的参数。我们表明，这些数据有效地约束了与均匀核物质性质相关的参数，而控制非均匀核物质的参数仅部分受到约束，需要额外的输入。此外，利用推断的参数分布，我们成功预测了208Pb的密度分布和结合能，而208Pb被排除在训练数据之外。这展示了该框架的预测能力。总之，这些结果确立了密度分布的贝叶斯分析作为将轻核的精确从头计算结果纳入EDF发展并加强两种方法之间联系的有前景的途径。
英文摘要
In nuclear many-body physics, energy density functional (EDF) theory is one of the most powerful approaches for describing finite nuclei and nuclear matter. However, its predictive capability depends on calibrating model parameters to experimental and observational data. In this work, we investigate an alternative approach: Constraining the parameters with the continuous density profiles of finite nuclei obtained from ab initio calculations. We apply Bayesian analysis to infer the parameters of Skyrme EDF from the density profiles and binding energies of 16O, 40Ca, and 48Ca. We show that the data effectively constrain the parameters associated with the properties of uniform nuclear matter, whereas those governing non-uniform nuclear matter remain partially constrained and require additional input. Furthermore, using the inferred parameter distributions, we successfully predict the density profiles and binding energy of 208Pb, which is excluded from the training data. This demonstrates the predictive capability of the framework. In conclusion, these results establish Bayesian analysis of density profiles as a promising route for incorporating accurate ab initio results of light nuclei into EDF development and strengthening the connection between both approaches.
Comments15 pages, 6 figures
