Simulation-based inference of black hole ringdowns in the time domain
arxiv(2024)
摘要
Gravitational waves emitted by a ringing black hole allow us to perform
precision tests of General Relativity in the strong field regime. With
improvements to our current gravitational wave detectors and upcoming
next-generation detectors, developing likelihood-free parameter inference
infrastructure is critical as we will face complications like non-standard
noise properties, partial data and incomplete signal modeling that may not
allow for an analytically tractable likelihood function. In this work, we
present a proof-of-concept strategy to perform likelihood-free Bayesian
inference on ringdown gravitational waves using simulation based inference.
Specifically, our method is based on truncated sequential neural posterior
estimation, which trains a neural density estimator of the posterior for a
specific observed data segment. We setup the ringdown parameter estimation
directly in the time domain. We show that the parameter estimation results
obtained using our trained networks are in agreement with well-established
Markov-Chain methods for simulated injections as well as analysis on real
detector data corresponding to GW150914. Additionally, to assess our approach's
internal consistency, we show that the density estimators pass a Bayesian
coverage test.
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