Real-time gravitational-wave science with neural posterior estimation
- URL: http://arxiv.org/abs/2106.12594v2
- Date: Tue, 30 May 2023 15:05:17 GMT
- Title: Real-time gravitational-wave science with neural posterior estimation
- Authors: Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke,
Alessandra Buonanno, Bernhard Sch\"olkopf
- Abstract summary: We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning.
We analyze eight gravitational-wave events from the first LIGO-Virgo Gravitational-Wave Transient Catalog.
We find very close quantitative agreement with standard inference codes, but with inference times reduced from O(day) to a minute per event.
- Score: 64.67121167063696
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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