Real-Time Gravitational Wave Science with Neural Posterior Estimation

We demonstrate unprecedented accuracy for rapid gravitational wave parameter estimation with deep learning. Using neural networks as surrogates for Bayesian posterior distributions, we analyze eight gravitational wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog and find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to 20 s per event. Our networks are trained using simulated data, including an estimate of the detector noise characteristics near the event. This encodes the signal and noise models within millions of neural-network parameters and enables inference for any observed data consistent with the training distribution, accounting for noise nonstationarity from event to event. Our algorithm-called DINGO-sets a new standard in fast and accurate inference of physical parameters of detected gravitational wave events, which should enable real-time data analysis without sacrificing accuracy.

Automated summary

Using deep learning, unprecedented accuracy is achieved in rapid gravitational wave parameter estimation. Neural networks serve as surrogates for Bayesian posterior distributions, resulting in analysis of gravitational wave events with close quantitative agreement compared to standard inference codes. Trained with simulated data, the networks encode signal and noise models, enabling fast and accurate inference for detected gravitational wave events.