This review provides a conceptual and technical survey of methods for parameter estimation of gravitational-wave signals in ground-based interferometers such as Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo. We introduce the framework of Bayesian inference and provide an overview of models for the generation and detection of gravitational waves from compact binary mergers, focusing on the essential features that are observable in the signals. Within the traditional likelihood-based paradigm, we describe various approaches for enhancing the efficiency and robustness of parameter inference. This includes techniques for accelerating likelihood evaluations, such as heterodyne/relative binning, reduced-order quadrature, multibanding, and interpolation. We also cover methods to simplify the analysis to improve convergence, via reparameterization, importance sampling, and marginalization. We end with a discussion of recent developments in the application of likelihood-free (simulation-based) inference methods to gravitational-wave data analysis.

中文翻译：

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从引力波信号推断二进制特性


本文对地基干涉仪（如激光干涉仪引力波天文台 （LIGO） 和 Virgo））中引力波信号参数估计的方法进行了概念和技术综述。我们介绍了贝叶斯推理的框架，并概述了从紧凑的二进制合并中产生和检测引力波的模型，重点介绍了信号中可观察到的基本特征。在传统的基于似然的范式中，我们描述了提高参数推理效率和稳健性的各种方法。这包括用于加速似然评估的技术，例如外差/相对分箱、降阶正交、多带和插值。我们还介绍了通过重新参数化、重要性抽样和边缘化来简化分析以提高收敛性的方法。最后，我们讨论了无似然（基于仿真）推理方法在引力波数据分析中的应用的最新进展。