论文标题

《 Graxy目录方法》的引力宇宙学指南

The Hitchhiker's guide to the galaxy catalog approach for gravitational wave cosmology

论文作者

Gair, Jonathan R., Ghosh, Archisman, Gray, Rachel, Holz, Daniel E., Mastrogiovanni, Simone, Mukherjee, Suvodip, Palmese, Antonella, Tamanini, Nicola, Baker, Tessa, Beirnaert, Freija, Bilicki, Maciej, Chen, Hsin-Yu, Dálya, Gergely, Ezquiaga, Jose Maria, Farr, Will M., Fishbach, Maya, Garcia-Bellido, Juan, Ghosh, Tathagata, Huang, Hsiang-Yu, Karathanasis, Christos, Leyde, Konstantin, Hernandez, Ignacio Magaña, Noller, Johannes, Pierra, Gregoire, Raffai, Peter, Romano, Antonio Enea, Seglar-Arroyo, Monica, Steer, Danièle A., Turski, Cezary, Vaccaro, Maria Paola, Vallejo-Peña, Sergio Andrés

论文摘要

储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。

We outline the ``dark siren'' galaxy catalog method for cosmological inference using gravitational wave (GW) standard sirens, clarifying some common misconceptions in the implementation of this method. When a confident transient electromagnetic counterpart to a GW event is unavailable, the identification of a unique host galaxy is in general challenging. Instead, as originally proposed by Schutz (1986), one can consult a galaxy catalog and implement a dark siren statistical approach incorporating all potential host galaxies within the localization volume. Trott & Hunterer 2021 recently claimed that this approach results in a biased estimate of the Hubble constant, $H_0$, when implemented on mock data, even if optimistic assumptions are made. We demonstrate explicitly that, as previously shown by multiple independent groups, the dark siren statistical method leads to an unbiased posterior when the method is applied to the data correctly. We highlight common sources of error possible to make in the generation of mock data and implementation of the statistical framework, including the mismodeling of selection effects and inconsistent implementations of the Bayesian framework, which can lead to a spurious bias.

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