论文标题
中子星的现象学EOS的贝叶斯推断，最近观察
Bayesian Inference of Phenomenological EoS of Neutron Stars with Recent Observations
论文作者
论文摘要
恒星内部的描述仍然是核天体物理学界的巨大挑战。综合知识仅限于密度区域，围绕hadronic物质的饱和$ρ_{0} = 2.8 \ times 10^{14} {\ rm \ g \ g \ g \ cm^{ - 3}} $，我们的核模型成功地应用了我们的核模型。当人们向更高的密度和极端条件转向五到二十倍$ρ_{0} $时，关于此类物体的微物理学几乎没有说。在这里，我们采用了马尔可夫链蒙特卡洛（MCMC）策略来访问中子星方程的多粒子三岩模型的变异性。通过对Hadronic物质的固定描述，我们探索了高密度制度的各种模型，导致恒星质量高达$ 2.5 \ m _ {\ odot} $。此外，我们还讨论了具有异性误差的贝叶斯功率回归模型的使用。来自激光干涉仪重力波观测站（LIGO）的EOS集用作输入，并将其视为用于测试情况的数据集。
The description of stellar interior remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter $ρ_{0} = 2.8\times 10^{14} {\rm\ g\ cm^{-3}}$, regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme conditions up to five to twenty times $ρ_{0}$, little can be said about the microphysics of such objects. Here, we employ a Markov Chain Monte Carlo (MCMC) strategy to access the variability of polytropic three-pircewised models for neutron star equation of state. With a fixed description of the hadronic matter, we explore a variety of models for the high density regimes leading to stellar masses up to $2.5\ M_{\odot}$. In addition, we also discuss the use of a Bayesian power regression model with heteroscedastic error. The set of EoS from the Laser Interferometer Gravitational-Wave Observatory (LIGO) was used as inputs and treated as data set for testing case.
