  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 a minute 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.
