Real-Time Gravitational Wave Science with Neural Posterior Estimation
Jun 23, 2021
12 pages
Published in:
- Phys.Rev.Lett. 127 (2021) 24, 241103
- Published: Dec 8, 2021
e-Print:
- 2106.12594 [gr-qc]
DOI:
- 10.1103/PhysRevLett.127.241103 (publication)
Report number:
- LIGO-P2100223
View in:
Citations per year
Abstract: (APS)
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 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.Note:
- 7+12 pages, 4+11 figures. [v2]: Minor updates to match published version, code available at https://github.com/dingo-gw/dingo
- gravitational radiation
- detector: noise
- neural network
- statistical analysis: Bayesian
- LIGO
- VIRGO
- data analysis method
- noise
- numerical calculations
- Monte Carlo: Markov chain
References(65)
Figures(33)
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