通过自动微分从观测数据重构中子星物态方程
Reconstructing the neutron star equation of state from observational data via automatic differentiation
AI总结：
本研究提出一种基于自动微分逆问题框架的深度学习方法，利用中子星观测数据重构致密物质物态方程，所得结果与传统方法及GW170817的观测约束一致。
摘要：
中子星的质量、半径、潮汐形变能力等可观测量是探测致密物质物态方程（EoS）的直接手段。本文提出一种新颖的深度学习方法，在求解逆问题的自动微分框架下优化物态方程。经训练的神经网络物态方程给出了压强与声速随质量密度变化关系的窄区间范围。所得结果与传统方法的结论一致，也与引力波事件GW170817推断出的潮汐形变能力观测约束相符。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
