Abstract:
On 14th September, 2015, the LIGO detectors at Hanford and Livingston observed a gravitational wave signal originating from the merger of two stellar mass black holes. Since then, LIGO has reported the observation of 11 binary mergers during the O1 and O2 observing runs, and 39 additional candidates during the first half of the third observing run, O3a. The detection of an increasing number of merger events with each observing run has led to the development of various phenomenological models to describe the population properties of the merging binary black holes. Following a Bayesian inference approach, one can marginalize over the parameters describing individual events to constrain the parameters of these population models.
The constraints obtained on the binary population parameters from direct detections of individual merger events are limited by the fact that with the current detector technology, it is possible to individually resolve compact binary mergers only upto a redshift z⩽1. However, by combining the data obtained from the individual mergers with indirect measurements of the stochastic gravitational wave background, which contains signatures of such merger events from unresolved distant sources, it is possible to obtain tighter constraints on the parameters describing the BBH populations.
In this thesis, I have studied a Bayesian inference technique to construct the joint likelihood function for combining data from the stochastic background with direct detections of gravitational waves from binary black hole mergers, to get better constraints on binary black hole population parameters.
