标题： 基于神经网络的中子星状态方程的望远镜光谱直接模拟推理 显示英文标题

标题： Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra

摘要： 中子星提供了一个独特的机会来研究在极端密度条件下强相互作用物质的特性。 中子星内部物质的复杂性及其状态方程无法直接观测到，但它们决定了质量、半径等整体属性，这些属性会影响恒星的热X射线辐射。 然而，望远镜光谱中的这些辐射也受到恒星距离、氢柱密度和有效表面温度的影响，而这些参数并不总是能得到很好的约束。 在估计状态方程时，必须考虑这些无关参数的不确定性。 在这项研究中，我们开发了一种新颖的方法，首次可以从望远镜观测数据中直接推断出状态方程和无关参数的完整后验分布。 该方法依赖于神经似然估计的使用，在此过程中，归一化流利用模拟的望远镜数据样本来学习中子星光谱的似然函数作为这些参数的函数，并结合哈密顿蒙特卡罗方法从相应的后验分布中高效采样。 我们的方法超越了以前方法的精度，通过提供完整的后验分布提高了结果的可解释性，并且自然适应未来几年预期的中子星观测数量的增长。 显示英文摘要

摘要： Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such as mass and radius, which affect the star's thermal X-ray emissions. However, the telescope spectra of these emissions are also affected by the stellar distance, hydrogen column, and effective surface temperature, which are not always well-constrained. Uncertainties on these nuisance parameters must be accounted for when making a robust estimation of the equation of state. In this study, we develop a novel methodology that, for the first time, can infer the full posterior distribution of both the equation of state and nuisance parameters directly from telescope observations. This method relies on the use of neural likelihood estimation, in which normalizing flows use samples of simulated telescope data to learn the likelihood of the neutron star spectra as a function of these parameters, coupled with Hamiltonian Monte Carlo methods to efficiently sample from the corresponding posterior distribution. Our approach surpasses the accuracy of previous methods, improves the interpretability of the results by providing access to the full posterior distribution, and naturally scales to a growing number of neutron star observations expected in the coming years.