LEVERAGING MACHINE LEARNING FOR GRAVITATIONAL WAVE DISCOVERIES: From Binary Black Hole Mergers to Probing High-Density Nuclear Matter
Date:
Machine Learning is finding diverse applications in the field of astrophysics and gravitational wave astronomy. This talk surveys our recent advancements in applying such methodologies. We demonstrate the utility of Deep Residual Networks (ResNets) in discovering new gravitational wave events from binary black hole mergers. Furthermore, we discuss the importance of using robust metrics for evaluating the sensitivity of detection pipelines and present the calculation of the astrophysical probability in the framework of a machine-learning detection code. Finally, we discuss the application of normalizing flows within Preconditioned Monte Carlo methods to significantly accelerate Bayesian parameter estimation for binary neutron star post-merger signals.