Drischler C, Furnstahl R J, Melendez J A, Phillips D R
Department of Physics, University of California, Berkeley, California 94720, USA.
Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Phys Rev Lett. 2020 Nov 13;125(20):202702. doi: 10.1103/PhysRevLett.125.202702.
We introduce a new framework for quantifying correlated uncertainties of the infinite-matter equation of state derived from chiral effective field theory (χEFT). 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 χEFT 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 χEFT 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.
我们引入了一个新框架，用于量化从手征有效场论（χEFT）导出的无限核物质状态方程的相关不确定性。通过具有基于物理的超参数的高斯过程进行贝叶斯机器学习，使我们能够有效地量化状态方程的理论不确定性，并将其传播到导出量中，例如χEFT截断误差。我们将此框架应用于最先进的多体微扰理论计算，该计算考虑了χEFT展开中高达四阶的核子 - 核子和三核子相互作用。这为中子星的关键量产生了首个具有统计稳健性的不确定性估计。我们给出了中子物质每粒子能量、压力和声速以及核对称能及其导数在高达两倍核饱和密度时的结果。在核饱和密度下，预测的对称能及其斜率与实验约束一致。
