Parameter Estimation Using Markov Chain Monte Carlo Methods for Gravitational Waves from Spinning Inspirals of Compact Objects.

摘要

Gravitational waves are on the verge of opening a brand new window on the Universe. However, gravitational wave astronomy comes with very unique challenges in data analysis and signal processing in order to lead to new discoveries in astrophysics. Among the sources of gravitational waves, inspiraling binary systems of compact objects, neutron stars and/or black holes in the mass range 1Msun--100Msun stand out as likely to be detected and relatively easy to model.;The detection of a gravitational wave event is challenging and will be a rewarding achievement by itself. After such a detection, measurement of source properties holds major promise for improving our astrophysical understanding and requires reliable methods for parameter estimation and model selection. This is a complicated problem, because of the large number of parameters (15 for spinning compact objects in a quasi-circular orbit) and the degeneracies between them, the significant amount of structure in the parameter space, and the particularities of the detector noise.;This work presents the development of a parameter-estimation and model-selection algorithm, based on Bayesian statistical theory and using Markov chain Monte Carlo methods for ground-based gravitational-wave detectors (LIGO and Virgo). This method started from existing non-spinning and single spin stand-alone analysis codes and was developed into a method able to tackle the complexity of fully spinning systems, and infer all spinning parameters of a compact binary. Not only are spinning parameters believed to be astrophysically significant, but this work has shown that not including them in the analysis can lead to biases in parameter recovery.;This work made it possible to answer several scientific questions involving parameter estimation of inspiraling spinning compact objects, which are addressed in the chapters of this dissertation.