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arXiv · 2106.12594

Real-time gravitational-wave science with neural posterior estimation

Maximilian Dax·Stephen R. Green·Jonathan Gair·Jakob H. Macke·Alessandra Buonanno·Bernhard Schölkopf
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Abstract

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.

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BibTeXRIS

Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf. 2021-06-23. Real-time gravitational-wave science with neural posterior estimation. https://doi.org/10.1103/physrevlett.127.241103

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