Quantifying uncertainties and correlations in the nuclear-matter equation of state
Quantifying uncertainties and correlations in the nuclear-matter equation of state
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DOI：
10.1103/physrevc.102.054315
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发表时间：
2020-11-11
影响因子：
3.1
通讯作者：
Phillips, D. R.
中科院分区：
文献类型：
--
作者：
Drischler, C.;Melendez, J. A.;Phillips, D. R.
我们执行统计严格的不确定性量化（UQ）手性有效场理论（左）应用于无限核物质高达两倍的核饱和密度。状态方程（EOS）基于高阶多体微扰理论计算，在chi EFT展开中核子-核子和三核子相互作用高达四阶。从这些计算中，我们新开发的贝叶斯机器学习方法提取相关EFT截断误差的大小和平滑特性。然后，我们提出了一个新的扩展，使用多任务机器学习来揭示不同质子分数下EOS之间的相关性。纯中子物质和对称核物质介质中chi EFT击穿尺度的推断与自由空间核子-核子散射的推断一致。这些重大进展使我们能够提供核饱和点的后验分布，并将理论不确定性传播到推导量：对称核物质的压力和不可压缩性，核对称能及其导数。通过统计诊断验证的结果表明，理解不同密度和不同可观测值之间的截断误差相关性对于可靠的UQ至关重要。这里开发的方法可以作为带注释的Jupyter笔记本公开获得。
We perform statistically rigorous uncertainty quantification (UQ) for chiral effective field theory (LEFT) applied to infinite nuclear matter up to twice nuclear saturation density. The equation of state (EOS) is based on high-order many-body perturbation theory calculations with nucleon-nucleon and three-nucleon interactions up to fourth order in the chi EFT expansion. From these calculations our newly developed Bayesian machine-learning approach extracts the size and smoothness properties of the correlated EFT truncation error. We then propose a novel extension that uses multitask machine learning to reveal correlations between the EOS at different proton fractions. The inferred in-medium chi EFT breakdown scale in pure neutron matter and symmetric nuclear matter is consistent with that from free-space nucleon-nucleon scattering. These significant advances allow us to provide posterior distributions for the nuclear saturation point and propagate theoretical uncertainties to derived quantities: the pressure and incompressibility of symmetric nuclear matter, the nuclear symmetry energy, and its derivative. Our results, which are validated by statistical diagnostics, demonstrate that an understanding of truncation-error correlations between different densities and different observables is crucial for reliable UQ. The methods developed here are publicly available as annotated Jupyter notebooks.