A practical Bayesian method for gravitational-wave ringdown analysis with multiple modes

doi:10.1038/s41550-025-02766-6

2026-01-15

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DOI：10.1038/s41550-025-02766-6

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引力波（GW）环响信号来自黑洞，编码了强场引力动力学中的关键信息，为理解黑洞性质提供了独特的视角。提高引力波探测器的灵敏度将能够从环响信号中提取多个准正则模态。然而，引入多个模态会显著扩大参数空间，给数据分析带来计算挑战。受连续引力波搜索中 $${\mathcal{F}}$$ -统计方法的启发，本文开发了一种增强参数边缘化方案的新算法，称为FIREFLY，该算法专为加速环响信号分析而设计。FIREFLY通过解析边缘化准正则模态的振幅和相位参数，降低计算成本，并将完整参数的贝叶斯推断时间从小时级缩短至分钟级，同时保持一致的后验概率和证据。当考虑更多准正则模态时，加速效果更为显著。该方法严格基于贝叶斯推断和重要性采样，具有统计可解释性，在先验选择上灵活，并兼容多种先进采样技术，从而为加速未来的引力波数据分析提供了新视角。一种新的贝叶斯框架用于分析来自黑洞的引力波环响信号，加速了多模态推断，增强了强引力场下快速且统计稳健的检验。

Keyword:

gravitational-wave

ringdown signals

quasi-normal modes

Bayesian inference

parameter-marginalization

期刊

Nature Astronomy

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14.3

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2.8K

被引数:

1.3W

机构

henan academy of sciences

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888

论文数: 467

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Peking University

学者数:

1.0W

论文数: 3.8K

被引数: 14.7W

Dalian University of Technology

学者数:

5.9W

论文数: 4.4W

被引数: 5.5W

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