Searches for Binary Black Hole Merger Signals in LIGO-Virgo Data

Abstract

The detection of gravitational wave has opened a new window to study astrophysical systems like merging black holes and neutron stars. Since the first detection in 2015, advances in detector sensitivity and data analysis have led to nearly a hundred confirmed events across the first three observing runs. As detectors become more sensitive, identifying real signals amid noise, especially complex or unusual noise, remains a major challenge. This thesis presents new methods to improve the detection of Gravitational-wave signals. First, a ranking method based on kernel density estimation is developed to classify candidate signals more accurately across the full parameter space. When tested on O3 data, this method recovers more true signals than earlier approaches and has been adopted in the PyCBC pipeline for use in the O4 run.