论文标题
标准化流量进行分层贝叶斯分析：重力波总体研究
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
论文作者
论文摘要
我们提出了通过标准化流量的参数化引力波总体建模框架（分层贝叶斯分析）的种群分布。我们首先在说明性实验上证明了该方法的优点，然后分析了最新的Ligo/处女座数据发布的四个参数：初级质量，次级质量，红移和有效自旋。我们的结果表明，尽管有较小且臭名昭著的嘈杂数据集，但观察到的引力波总体恢复结构的后验预测分布（假设先前是流动参数的先验），与强大的先前现象学建模结果一致，而对较小灵活模型引入的偏见则不太容易受到敏感。因此，即使数据高度嘈杂，该方法也形成了人群推理分布的有前途的灵活，可靠的替代。
We propose parameterizing the population distribution of the gravitational wave population modeling framework (Hierarchical Bayesian Analysis) with a normalizing flow. We first demonstrate the merit of this method on illustrative experiments and then analyze four parameters of the latest LIGO/Virgo data release: primary mass, secondary mass, redshift, and effective spin. Our results show that despite the small and notoriously noisy dataset, the posterior predictive distributions (assuming a prior over the parameters of the flow) of the observed gravitational wave population recover structure that agrees with robust previous phenomenological modeling results while being less susceptible to biases introduced by less flexible models. Therefore, the method forms a promising flexible, reliable replacement for population inference distributions, even when data is highly noisy.
