Speaker: Maximilian Dax, PhD Student at the Max Planck Institute for Intelligent Systems
Inferring astrophyscial parameters from gravitational wave (GW) measurements is a central task in GW analysis. Standard inference methods, based e.g. on Markov chain Monte Carlo (MCMC), require days of computation for the analysis of a single GW event. In this talk I present our new approach DINGO that reduces this inference time to 20 seconds per event by using conditional normalizing flows. I then explain how physical symmetries can be used to enhance the accuracy of the inference networks. Finally, I demonstrate on real GW event data that our likelihood-free approach produces indistinguishable results from MCMC while being 1000 times faster.
Dax et al. Real-Time Gravitational Wave Science with Neural Posterior Estimation. Phys.Rev.Lett. 127, 241103 (2021)
Dax et al. Group equivariant neural posterior estimation. ICLR 2022
Watch Back ›