论文标题
通过自动差异从观察数据中重建状态的中子星方程
Reconstructing the neutron star equation of state from observational data via automatic differentiation
论文作者
论文摘要
中子星可观察物等质量，半径和潮汐变形性是对状态〜（EOS）的密集物质方程的直接探针。提出了一种在解决反问题的自动分化框架中优化EOS的新型深度学习方法。训练有素的神经网络EOS产生狭窄的频带，以与质量密度的函数之间的压力和速度之间的关系。结果与从常规方法获得的结果以及从重力波事件GW170817推断出的潮汐变形性上的观察性变形。
Neutron star observables like masses, radii, and tidal deformability are direct probes to the dense matter equation of state~(EoS). A novel deep learning method that optimizes an EoS in the automatic differentiation framework of solving inverse problems is presented. The trained neural network EoS yields narrow bands for the relationship between the pressure and speed of sound as a function of the mass density. The results are consistent with those obtained from conventional approaches and the observational bound on the tidal deformability inferred from the gravitational wave event, GW170817.
