AI 中文总结
该研究提出用高斯过程重建不完整星系目录的方法，可提升暗信号宇宙学中哈勃常数约束的精度，在24%和8%完整度目录下精度分别提升23%、37%，最高达66%。
AI 中文摘要
我们提出了一种新颖框架，用于改进暗信号宇宙学，方法是将高斯过程（GP）应用于不完整星系目录的视线（LOS）重建。在从引力波（GW）暗信号推断哈勃常数$H_0$的标准星系目录方法中，缺失星系通常被假定为在共动体积中遵循均匀分布，该假设丢弃了对宇宙学推断至关重要的星系红移成团信息。我们转而将视线星系红移分布建模为来自GP实现的非参数函数，通过明确考虑巡天选择函数的分层贝叶斯似然，将其拟合到观测到的不完整目录。将该方法应用于延伸至红移$z\leq0.4$的模拟GW和星系目录时，当使用24%完整度的星系目录时，我们的方法得到的$H_0$约束的精度平均比标准均匀补全高23%；对于8%完整度的目录，精度平均高37%。在GP最有效重建均匀补全无法捕捉的红移过密和欠密特征的配置中，精度提升最大，达到66%。
英文摘要
We present a novel framework for improving dark siren cosmology by applying a Gaussian process (GP) to the line-of-sight (LOS) reconstruction of incomplete galaxy catalogs. In the standard galaxy catalog method for inferring the Hubble constant $H_0$ from gravitational-wave (GW) dark sirens, missing galaxies are typically assumed to follow a uniform distribution in comoving volume, an assumption that discards galaxy redshift clustering information crucial for cosmological inference. We propose instead to model the LOS galaxy redshift distribution as a non-parametric function drawn from a GP realization, which is fitted to the observed incomplete catalog via a hierarchical Bayesian likelihood that explicitly accounts for the survey selection function. Applied to mock GW and galaxy catalogs extending up to redshift $z\leq0.4$, our method yields $H_0$ constraints that are on average 23% more precise than the standard homogeneous completion when using a 24%-complete galaxy catalog, and 37% more precise for an 8%-complete catalog. The largest improvement, reaching 66%, is obtained in configurations where the GP most effectively reconstructs the redshift over- and under-density features that the homogeneous completion fails to capture.
Comments16 pages, 11 figures
