Gravitational-wave astronomy has entered a regime where it can extract information about the population properties of the observed binary black holes. The steep increase in the number of detections will offer deeper insights, but it will also significantly raise the computational cost of testing multiple models. To address this challenge, we propose a procedure that first performs a non-parametric (data-driven) reconstruction of the underlying distribution, and then remaps these results onto a posterior for the parameters of a parametric (informed) model. The computational cost is primarily absorbed by the initial non-parametric step, while the remapping procedure is both significantly easier to perform and computationally cheaper. In addition to yielding the posterior distribution of the model parameters, this method also provides a measure of the model's goodness-of-fit, opening for a new quantitative comparison across models.

翻译：引力波天文学已进入能够提取观测到的双黑洞群体属性信息的阶段。检测数量的急剧增长将带来更深入的见解，但也将显著提高测试多个模型的计算成本。为应对这一挑战，我们提出一种方法：首先对基础分布进行非参数（数据驱动）重构，然后将这些结果重新映射到参数化（信息）模型参数的后验分布上。计算成本主要由初始非参数步骤承担，而重新映射过程不仅执行起来显著更容易，计算成本也更低。除了得到模型参数的后验分布外，该方法还提供了模型拟合优度的度量，为模型间的定量比较开辟了新途径。