论文标题
我们如何了解中子恒星内密度的状态中子方程？具有相关不确定性的贝叶斯方法
How well do we know the neutron-matter equation of state at the densities inside neutron stars? A Bayesian approach with correlated uncertainties
论文作者
论文摘要
我们引入了一个新的框架，用于量化来自手性有效野外理论的无限摩尔人方程的相关不确定性（$χ$ eft）。贝叶斯机器通过基于物理的高参数的高斯过程学习，使我们能够有效地量化和传播状态方程的理论不确定性，例如$χ$ eft截断错误，以衍生数量。我们将此框架应用于最先进的多体扰动理论，并在$χ$ eft扩展中使用核子核子和三核相互作用进行了高达第四阶。这产生了中子星的关键量的第一个统计上稳定的不确定性估计。我们为每个粒子的能量，中子物质的压力和速度以及核对称能及其导数提供了最多两倍的核饱和密度。在核饱和密度下，预测的对称能及其斜率与实验约束一致。
We introduce a new framework for quantifying correlated uncertainties of the infinite-matter equation of state derived from chiral effective field theory ($χ$EFT). Bayesian machine learning via Gaussian processes with physics-based hyperparameters allows us to efficiently quantify and propagate theoretical uncertainties of the equation of state, such as $χ$EFT truncation errors, to derived quantities. We apply this framework to state-of-the-art many-body perturbation theory calculations with nucleon-nucleon and three-nucleon interactions up to fourth order in the $χ$EFT expansion. This produces the first statistically robust uncertainty estimates for key quantities of neutron stars. We give results up to twice nuclear saturation density for the energy per particle, pressure, and speed of sound of neutron matter, as well as for the nuclear symmetry energy and its derivative. At nuclear saturation density the predicted symmetry energy and its slope are consistent with experimental constraints.
