Enabling Rapid Discovery of Gravitational Waves Using Machine Learning

The simultaneous observation of gravitational waves and prompt electromagnetic emissions from mergers of compact objects can help reveal properties of extreme matter and spacetime during and immediately after the coalescence. Such multimessenger observations rely on rapid detection and sky localization of gravitational waves, often requiring alerts to be sent out before the merger. For this thesis work, I have developed machine-learning models for rapid pre- and post-merger sky localization, and waveform extraction from detector data. I have conducted studies on simulated and real detector data and demonstrated the speed and accuracy of machine-learning models for rapid gravitational wave discovery.