Speaker
Description
We develop a framework for accelerated parameter estimation for lensed gravitational waves in the wave-optics regime. To model these effects, we evaluate the amplification factor using GLoW, an accurate code that computes the frequency-dependent amplification factor for generic lens models and arbitrary impact parameters depending on the lens configuration. To extract physical parameters from the lensed gravitational wave signals, we employ the DINGO code, a machine learning framework based on neural posterior estimation to evaluate the posterior probability. We validate the method using point lens. By applying this method we can efficiently perform parameter estimation on diffracted gravitational wave signals, recovering the mass of the lens and the impact parameter independently.
