发表机构
Center for Computational Relativity and Gravitation, Rochester Institute of Technology（计算相对论与引力中心，罗切斯特理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究构建了一个自动可微的物理唯象模型，嵌入 gwkokab 框架，用于从引力波数据中联合推断层级致密双星种群的出生与相互作用参数，并解释 GWTC-5.0 观测特征。
AI 中文摘要
引力波（GW）普查中包含的质量和自旋结构，与致密环境中通过重复并合形成的黑洞的贡献相一致。将这些结构与形成物理联系起来，需要既具有物理可解释性又能在种群推断中易于处理的模型。我们构建了一个自动可微的、基于物理的唯象模型，其中每个致密环境由一个凝聚响应表示，而这样的环境种群产生一个可观测的并合率密度。嵌入在 gwkokab 泊松似然框架中，该模型能够从 GW 普查中联合推断出生种群和相互作用参数。应用于 GWTC-5.0 时，该框架展示了为何简单的成对凝聚模型难以重现观测到的高质量、可比质量种群，并针对数据测试了替代的相互作用结构，同时保留了一个明确建模的出生组分。
英文摘要
The gravitational-wave (GW) census contains mass and spin structure consistent with contributions from black holes assembled through repeated mergers in dense environments. Connecting that structure to formation physics requires models that are both physically interpretable and tractable within population inference. We construct an autodifferentiable, physics-based phenomenological model in which each dense environment is represented by a coagulation response and a population of such environments produces an observable merger-rate density. Embedded in the gwkokab Poisson-likelihood framework, this model enables joint inference of natal-population and interaction parameters from the GW census. Applied to GWTC-5.0, the framework shows why simple pairwise coagulation models struggle to reproduce the observed high-mass, comparable-mass population and tests alternative interaction structures against the data, while retaining an explicitly modeled natal component.
Comments30 pages, 12 figures; main paper only
