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

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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.