从未来中子星半径的高精度数据对致密物质物态方程（EOS）精细特征进行贝叶斯推断
Bayesian Inference of fine-features of dense matter EOS from future high-precision data of neutron star radii
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中文总结 AI 辅助
本研究在贝叶斯框架下，利用元模型物态方程和模拟高精度中子星半径数据，探究未来高精度观测对致密物质物态方程精细特征的约束能力，并报告相关研究亮点。
中文摘要 AI 辅助
未来高精度X射线和引力波天文台有望以优于约0.1 km的精度测量中子星（NS）的半径，但目前尚不清楚物态方程（EOS）的哪些特定方面以及它们将被约束到何种精度。近期一项研究在采用元模型EOS和模拟高精度NS数据的贝叶斯框架内，对强子相和夸克相的NS物质EOS参数及其之间转变的后验概率分布函数（PDFs）展开了研究，本文报告了这些研究的若干重要结果。
英文摘要
Future high-precision X-ray and gravitational wave observatories are expected to measure the radii of neutron stars (NSs) with an accuracy better than about 0.1 km. However, it remains unclear what particular aspects of the Equation of State (EOS) and to what precision they will be better constrained. Within a Bayesian framework using a meta-model EOS and mock high-precision NS data, the posterior probability distribution functions (PDFs) of NS matter EOS parameters for both hadronic and quark phases and the transition between them were recently studied. We report here a few highlights of these studies.
