Speaker: Stephen Green (University of Nottingham)
Title: Neural posterior estimation for gravitational-wave inference
Abstract: I will describe how deep learning and simulation-based inference address gravitational-wave data analysis challenges, including high event rates and rapid electromagnetic follow-up. The approach uses simulated data to train neural networks, such as normalizing flows, to accurately represent posterior distributions. Once trained, these models enable extremely rapid inference—reducing analyses to seconds. I will highlight recent advances in population inference and binary neutron star parameter estimation, demonstrating the promise of these techniques for next-generation detectors.
Room: Sala de Reuniões e Seminários (2-8.3, 2nd Floor of Physics Building)