Understanding the merger of two Black Holes using Gravitational waves

I will present one of the important predictions of General Relativity; Gravitational Waves. LIGO Lab was first conceptualized by Rainer Weiss and his team which eventually received funding from Na- tional Science Foundation (NSF) and other organizations. This discovery was awarded a 2017 Nobel Prize in Physics, with one half belonging to Rainer Weiss and the other half split between Kip Thorne and Barry Bar- ish, the prime contributors for the conception of these detectors. The first LIGO detection paper, which observed the Gravitational waves from the coalescence of two black holes, is one of the most cited papers in the his- tory of Physics. In my work, I have done the Event Detection and parameter estimation for three different sources GW150914, GW190521 and GW200302 using Bayesian Inference. Bayesian parameter estimation is fast becoming the language of gravitational-wave astronomy. It is the method by which gravitational-wave data is used to infer the sources’ astrophysical proper- ties. We used a user-friendly Bayesian inference library for gravitational- wave astronomy, BILBY which was published in 2019 by Ashton et al. This alongside with LaLsimulation used in PYTHON an provides expert-level parameter estimation infrastructure with straightforward syntax and tools that facilitate use by beginners. It allows users to perform accurate and re- liable gravitational-wave parameter estimation on both publicly available data from LIGO/Virgo and simulated data. Finally, the relation between the time to merge for black holes with different masses is illustrated.