Kavli Affiliate: Lijing Shao
| First 5 Authors: Yiming Dong, Ziming Wang, Hai-Tian Wang, Junjie Zhao, Lijing Shao
| Summary:
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 $F$-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.
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