BibTeX

@article{2404.11373v3,
Author        = {Costantino Pacilio and Swetha Bhagwat and Roberto Cotesta},
Title         = {Simulation-based inference of black hole ringdowns in the time domain},
Eprint        = {2404.11373v3},
DOI           = {10.1103/PhysRevD.110.083010},
ArchivePrefix = {arXiv},
PrimaryClass  = {gr-qc},
Abstract      = {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 nonstandard 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.},
Year          = {2024},
Month         = {Apr},
Note          = {Phys. Rev. D 110 (2024) no.8, 083010},
Url           = {http://arxiv.org/abs/2404.11373v3},
File          = {2404.11373v3.pdf}
}