Neural posterior estimation for gravitational-wave inference

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.