On September 14th, 2015, the remnants of two massive stars collided, producing gravitational waves that rung out accross the universe. It has been discovered that this is fairly common. Einstein’s Theory of General Relativity, first predicted gravitational waves. After a century of scientific discovery, engineering marvels, and the proliferation of technology, scientists ultimately built an instrument which can detect them. The Laser Interferometer Gravitational-Wave Observatory (LIGO) detected this event, and many more in LIGO’s first and second observational runs.
Scientists model the signals that have been detected in order to study the properties of these systems. These parameters, e.g. the mass and spin of each black hole, will in turn yield parts of the recipe for the universe. They may one day grant us insight on the Hubble Constant and the stellar Initial Mass Function. Before these events can be useful in that way, scientists need to apply astrophysical and statistical models to estimate those parameters.
This thesis has three goals: (i) To summarize the Bayesian approach adopted by the Rochester Institute of Technology (RIT) parameter estimation group. (ii) To discuss the application of the Kolmogorov-Smirnov test as a goodness of fit test, in order to reduce computational waste and validate parameter estimation results. (iii) To apply parameter estimation techniques, combined with this test, in order to quantify the needs of the algorithm used by the RIT group, as well as justify its methodology. In the pursuit of these goals, I will apply Bayesian statistical theory, test the limits of our techniques, and explore astrophysical models and a real gravitational wave event
