论文标题
可扩展的随机森林回归剂，用于结合状态测量的中子星程：GW170817和GW190425的案例研究
A scalable random forest regressor for combining neutron-star equation of state measurements: A case study with GW170817 and GW190425
论文作者
论文摘要
二进制中子恒星聚结的引力波观测通过测量每个中子星的潮汐变形，从而限制了状态的中子星程。该变形取决于潮汐变形参数$λ$，该参数使用第一二进制中子星星重力波观测（GW170817）约束。现在，通过测量第二个二进制中子星GW190425，我们可以结合不同的重力波测量，以获得对状态中子星方程的更严格的约束。在本文中，我们结合了GW170817和GW190425的数据，以将约束放在状态的中子星程上。为了促进该计算，我们使用机器学习算法得出了每个事件的插值边缘化似然。我们公开可用的这些可能性允许从多个重力波信号的结果轻松组合。使用这些新数据产品，我们发现信托1.4 $ m_ \ odot $中子星的半径约束至$ 11.6^{+1.6} _ { - 0.9} $ km，以90％的置信度，核饱和度的压力两倍，将核饱和密度约束至$ 3.1^{+3.1^{+3.1^{+3.1} {+3.1} {34} _ {-1.3} {-1.3} Dyne/cm $^2 $以90％的信心。该结果由GW170817主导，与其他作品的发现一致。
Gravitational-wave observations of binary neutron star coalescences constrain the neutron-star equation of state by enabling measurement of the tidal deformation of each neutron star. This deformation is determined by the tidal deformability parameter $Λ$, which was constrained using the first binary neutron star gravitational-wave observation, GW170817. Now, with the measurement of the second binary neutron star, GW190425, we can combine different gravitational-wave measurements to obtain tighter constraints on the neutron-star equation of state. In this paper, we combine data from GW170817 and GW190425 to place constraints on the neutron-star equation of state. To facilitate this calculation, we derive interpolated marginalized likelihoods for each event using a machine learning algorithm. These likelihoods, which we make publicly available, allow for results from multiple gravitational-wave signals to be easily combined. Using these new data products, we find that the radius of a fiducial 1.4 $M_\odot$ neutron star is constrained to $11.6^{+1.6}_{-0.9}$ km at 90% confidence and the pressure at twice the nuclear saturation density is constrained to $3.1^{+3.1}_{-1.3}\times10^{34}$ dyne/cm$^2$ at 90% confidence. This result is dominated by GW170817 and is consistent with findings from other works.
