AI 中文总结
针对手征模型拉格朗日参数难校准及计算昂贵问题，开发贝叶斯推理框架，用神经网络代理近似加速映射，应用于手征平均场模型，发现可行解分布在特定参数区域，数据对耦合组合约束更强，强调结合地面与天体物理信息的必要。
AI 中文摘要
核相互作用的手征模型提供了对致密物质的近似唯象描述，但其拉格朗日参数难以校准，且重复模型评估计算成本高。为此，我们开发了一个贝叶斯推理框架，通过神经网络代理近似加速从模型参数到核与粒子可观测量的重复映射，以识别与核饱和性质和真空实验约束一致的参数区域。我们将该框架应用于具有新广义四次矢量自相互作用扇区的手征平均场模型，发现可行解虽少但在参数空间特定区域广泛分布，数据对耦合组合的约束强于单个拉格朗日参数，结果的简并性表明不同的饱和兼容模型对致密核物质和中子星可能有定性不同的描述，凸显结合地面和天体物理信息的必要性。
英文摘要
Chiral models of nuclear interactions provide approximate, phenomenological descriptions of dense matter that respect the symmetries of quantum chromodynamics. Their Lagrangian parameters, however, are difficult to calibrate because these models are not controlled effective theories. Furthermore, repeated model evaluations are computationally expensive, and most parameter choices fail to reproduce acceptable saturation properties or hadron masses in vacuum. To address this, we develop a Bayesian inference framework to identify parameter regions consistent with nuclear saturation properties and vacuum experimental constraints. We implement this framework through a neural-network surrogate approximation that accelerates the repeated mapping from model parameters to nuclear and particle observables. Our fully-modular, neural-accelerated Bayesian framework interfaces the open-source MUSES Calculation Engine, the Bilby inference library, and the PyTorch machine-learning toolkit. We then apply the framework to the chiral mean-field model with a new generalized quartic vector self-interaction sector. We find that viable solutions are rare but broadly distributed within certain regions of parameter space, with the data constraining combinations of couplings more strongly than individual Lagrangian parameters. The resulting degeneracies imply that distinct saturation-compatible models can lead to qualitatively different descriptions of dense nuclear matter and, thus, of neutron stars, highlighting the need to combine terrestrial and astrophysical information.
Comments29 pages, 17 figures
