Bayesian Techniques for Astrophysical Inference from Gravitational-waves of Compact Binary Coalescences: an Application to the Third LIGO-Virgo-KAGRA

LIGO-Virgo探测器网络对引力波的观测使用了贝叶斯参数估计和模型选择技术来表征位于信号原点的双星黑洞和双星中子星。这些方法是引力波天体物理学新领域的核心。这个项目将改进现有的技术，以促进我们对下一次引力波观测的理解。特别是，探测器的噪声特性没有得到很好的理解，一个可能的重点将是开发噪声模型和探测器特有的战略，以便能够利用关于探测器数据和监测系统的数据挖掘和机器学习技术，从引力波观测中进行准确的测量。另一个研究方向涉及将多个引力波观测汇集在一起。通过这种方式，贝叶斯分析可以推断潜在的分布参数，如天体物理种群的共同属性、引力理论的约束以及物质最密集状态方程的测量。这将包括低于阈值的事件；个别不太重要但综合起来具有很大统计能力的观测结果。而探测器的噪声特性对其中很大一部分人来说将是重要的。

The observations of gravitational waves by the LIGO-Virgo detector network have used Bayesian parameter estimation and model selection techniques to characterises the binary-black-holes and the binary-neutron-star at the origin of the signals. Those methods are central to the new field of gravitational-wave astrophysics. This project will improve existing techniques to advance our understanding of the next gravitational-wave observations. In particular, the detector's noise properties are not well understood, and a possible focus will be to develop noise models and detector-specific strategies to enable accurate measurements from gravitational-wave observation, using data mining and machine learning techniques on both detector data and monitoring systems. Another research direction involves pooling together multiple gravitational-wave observations. That way, a Bayesian analysis can infer underlying distribution's parameters, such as the common properties of astrophysical populations, constraints of theories of gravity, and measurements of the equation-of-state of matter at its densest. This will include sub-threshold events; observations which individually are not so significant but taken in aggregate possess great statistical power. And the detectors' noise properties will be important for a very large fraction of them.