机器学习群体合成校准并合率在IllustrisTNG300中的引力波偏置
Gravitational Wave Bias in IllustrisTNG300 from Machine-Learned Population-Synthesis Calibrated Merger Rates
- University of Waterloo（滑铁卢大学）
- Perimeter Institute for Theoretical Physics（佩里尔理论物理研究所）
- Massachusetts Institute of Technology（麻省理工学院）
- University of Cambridge（剑桥大学）
- Indian Institute of Science（印度科学学院）
- The Ohio State University（俄亥俄州立大学）
- California Institute of Technology（加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究开发基于模拟的框架，利用机器学习模拟器在IllustrisTNG300中填充引力波源，估计双黑洞并合的引力波偏置，发现其强于星系偏置且受金属丰度等属性影响，为未来实验提供解释框架。
AI中文摘要：
引力波（GW）源的大尺度成团性为宇宙学和致密双星天体物理学提供了独立的探针。解释未来的引力波成团测量需要理解并合率如何依赖于宿主星系属性和环境。我们开发了一个基于模拟的框架来模拟双黑洞（BBH）并合的成团性并估计相应的引力波偏置。我们使用在GALAXYRATE数据集上训练的机器学习模拟器将引力波源填充到IllustrisTNG300中，该数据集提供了与星系形成历史相关联的群体合成校准并合率。这使得我们能够构建具有物理动机的模拟引力波目录，其中包含依赖于恒星质量、恒星形成率、金属丰度和红移的星系特定BBH并合率。我们将此框架与一个更简单的唯象模型进行比较，在该模型中并合率仅依赖于恒星质量。在两种方法中，引力波源的偏置都比整体星系群更强，反映了BBH并合倾向于发生在质量更大、成团性更强的暗物质晕中。机器学习模型还产生了比仅恒星质量加权更强的引力波偏置尺度依赖性，表明额外的宿主属性影响引力波成团性。我们进一步发现金属丰度与引力波偏置和星系偏置均呈正相关。这些结果强调了具有物理动机的并合率模型对于引力波大尺度结构分析的重要性，并为解释来自爱因斯坦望远镜和宇宙探索者等实验的未来成团测量提供了框架。
英文摘要：
The large scale clustering of gravitational wave (GW) sources offers an independent probe of cosmology and compact-binary astrophysics. Interpreting future GW clustering measurements requires understanding how merger rates depend on host-galaxy properties and environment. We develop a simulation-based framework to model the clustering of binary black hole (BBH) mergers and estimate the corresponding GW bias. We populate IllustrisTNG300 with GW sources using a machine-learning emulator trained on the GALAXYRATE data set, which provides population-synthesis calibrated merger rates linked to galaxy formation histories. This enables physically motivated mock GW catalogs with galaxy-specific BBH merger rates that depend on stellar mass, star formation rate, metallicity, and redshift. We compare this framework to a simpler phenomenological model in which the merger rate depends only on stellar mass. In both approaches, GW sources are more strongly biased than the overall galaxy population, reflecting the preference for BBH mergers to occur in more massive, more strongly clustered halos. The machine-learned model also produces stronger scale dependence in the GW bias than stellar-mass weighting alone, indicating that additional host properties affect GW clustering. We further find that metallicity correlates positively with both GW and galaxy bias. These results highlight the importance of physically motivated merger rate models for GW large scale structure analyses and provide a framework for interpreting future clustering measurements from experiments such as the Einstein Telescope and Cosmic Explorer.
