Speaker
Hunter Gabbard
(University of Glasgow)
Description
Gravitational wave detection is now commonplace and as the sensitivity of the global network of GW detectors improves, we will observe O(100)s of transient GW events per year. The current methods used to estimate their source parameters employ optimally sensitive but computationally costly Bayesian inference approaches where typical analyses have taken between 6 hours and 5 days. Here we show that a conditional variational autoencoder pre-trained on binary black hole signals can return Bayesian posterior probability estimates 6 orders of magnitude faster than existing techniques.
