AI 中文总结
该研究提出基于手征核子相互作用的微观计算，引入GPDiff软件包实现高斯过程回归，约束核物态方程，为核EOS不确定性量化提供通用工具。
AI 中文摘要
我们基于手征核子-核子相互作用和三核子相互作用，从零温度下的微观非对称物质计算中得到了核物态方程（EOS）的约束，这些约束包括饱和点、不可压缩性的同位旋依赖性、对称能，以及中子星物质的壳-核转变密度。为了量化并将来自含噪多体计算的相关不确定性传播到导出观测量，我们引入了GPDiff，这是一个基于JAX的高效Python软件包，用于带有自动微分的多元高斯过程（GP）回归。训练完成后，GPDiff可实现对EOS及其相对于输入变量的任意阶导数（包括混合偏导数）的联合预测。在本次初始应用中，我们分析了近期关于非对称物质的高阶多体微扰理论计算（范围约为两倍饱和密度），并探索了一类依赖于输入的核函数——非平稳变化-表面核函数，用于对EOS进行建模。GPDiff广泛适用于零温度及有限温度下的微观核EOS计算，为基于GP的核EOS不确定性量化与推断提供了通用软件包。
英文摘要
We present constraints on the nuclear equation of state (EOS) from microscopic asymmetric matter calculations at zero temperature based on chiral nucleon-nucleon and three-nucleon interactions. The constraints include the saturation point, the isospin dependence of the incompressibility, and the symmetry energy, as well as the crust-core transition density of neutron-star matter. To quantify and propagate correlated uncertainties from noisy many-body calculations to derived observables, we introduce GPDiff, an efficient JAX-based Python package for multivariate Gaussian process (GP) regression with automatic differentiation. After training, GPDiff enables joint predictions of the EOS and derivatives of arbitrary order with respect to the input variables, including mixed partial derivatives. In this initial application, we analyze recent high-order many-body perturbation theory calculations of asymmetric matter up to about twice saturation density and explore nonstationary change-surface kernels, a class of input-dependent kernels, for modeling the EOS. GPDiff is broadly applicable to microscopic nuclear EOS calculations at zero and finite temperature and provides a versatile package for GP-based uncertainty quantification and inference of the nuclear EOS.
Comments30 pages, 13 figures, 2 tables
