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贝叶斯参数估计和模型选择技术来表征信号起源处的双黑洞和双中子星。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，提高我们对下一次引力波观测的理解。特别是，探测器的噪声特性还没有被很好地理解，一个可能的重点将是开发噪声模型和探测器特定的策略，以便在探测器数据和监测系统上使用数据挖掘和机器学习技术，从引力波观测中进行精确测量。另一个研究方向是汇集多个引力波观测数据。这样，贝叶斯分析就可以推断出潜在的分布参数，比如天体物理种群的共同特性、引力理论的约束，以及密度最大时物质的状态方程的测量。这将包括次阈值事件；个别的观察结果不那么重要，但综合起来看，具有很大的统计威力。而且探测器的噪声特性对其中很大一部分来说都很重要。

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.