A practical Bayesian method for gravitational-wave ringdown analysis with multiple modes
arXiv:2502.01093 · doi:10.1038/s41550-025-02766-6
Abstract
Gravitational-wave (GW) ringdown signals from black holes (BHs) encode crucial information about the gravitational dynamics in the strong-field regime, which offers unique insights into BH properties. In the future, the improving sensitivity of GW detectors is to enable the extraction of multiple quasi-normal modes (QNMs) from ringdown signals. However, incorporating multiple modes drastically enlarges the parameter space, posing computational challenges to data analysis. Inspired by the -statistic method in the continuous GW searches, we develope an algorithm, dubbed as FIREFLY, for accelerating the ringdown signal analysis. FIREFLY analytically marginalizes the amplitude and phase parameters of QNMs to reduce the computational cost and speed up the full-parameter inference from hours to minutes, while achieving consistent posterior and evidence. The acceleration becomes more significant when more QNMs are considered. Rigorously based on the principle of Bayesian inference and importance sampling, our method is statistically interpretable, flexible in prior choice, and compatible with various advanced sampling techniques, providing a new perspective for accelerating future GW data analysis.
21 pages, 14 figures
References in corpus (33)
- Observation of Gravitational Waves from a Binary Black Hole Merger
- GW170817: Measurements of Neutron Star Radii and Equation of State
- Quasinormal modes of black holes and black branes
- TianQin: a space-borne gravitational wave detector
- Tests of general relativity with GW150914
- A gravitational-wave standard siren measurement of the Hubble constant
- Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
- Sensitivity Studies for Third-Generation Gravitational Wave Observatories
- Is the gravitational-wave ringdown a probe of the event horizon?
- Data analysis of gravitational-wave signals from spinning neutron stars. I. The signal and its detection
- Detection, Measurement and Gravitational Radiation
- An introduction to Bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models
- The SXS Collaboration catalog of binary black hole simulations
- Black Hole Spectroscopy: Testing General Relativity through Gravitational Wave Observations
- Testing the no-hair theorem with GW150914
- Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation
- Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
- Real-time gravitational-wave science with neural posterior estimation
- Matched-filtering and parameter estimation of ringdown waveforms
- Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
- Testing the black-hole area law with GW150914
- Tests of General Relativity with GWTC-3
- Ringdown overtones, black hole spectroscopy, and no-hair theorem tests
- Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
- Mixing of spherical and spheroidal modes in perturbed Kerr black holes
- Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics
- EMRI data analysis with a phenomenological waveform
- Landscape of stellar-mass black-hole spectroscopy with third-generation gravitational-wave detectors
- Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors
- Simulation-based inference of black hole ringdowns in the time domain
- Bayesian -statistic-based parameter estimation of continuous gravitational waves from known pulsars
- Reanalyzing the ringdown signal of GW150914 using the F-statistic method
- Gravitational wave ringdown analysis using the -statistic