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2021

Real-time gravitational-wave science with neural posterior estimation

Dax, Maximilian, Green, Stephen R., Gair, Jonathan et al.

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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.

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