Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder
Abstract
Ringdown gravitational waves from binary black hole mergers can be modeled as superpositions of quasinormal modes (QNMs), whose frequencies and excitation factors encode properties of the remnant Kerr black hole. Reliable extraction of multiple QNM components is challenging because of mode overlap and noise. We develop an autoencoder-based framework for multi-component QNM analysis, in which the latent space is trained to represent the physical parameters of individual modes, enabling waveform denoising and parameter estimation within a common framework. Using controlled model waveforms constructed as finite sums of Kerr QNMs with recently established high-precision frequencies and excitation factors, including their nontrivial spin dependence near resonant excitation, we assess the method across partitioned spin intervals. The model achieves good in-domain waveform reconstruction and parameter recovery for the two longest-lived components of eight-component input waveforms, while its performance degrades when the validation spins lie far outside the training range. In a selected spin interval, the framework also recovers the 32 parameters of an eight-component waveform with good overall agreement. These results demonstrate the feasibility of physics-informed autoencoder-based inference for a prescribed multi-component ringdown waveform family and motivate further tests with progressively more realistic signals.
Related
- Ringdown and echoes from compact objects: Debye series and Debye quasinormal modes
- Exact Cancellation of Horizon Modes in Kerr Ringdown
- Pole Skipping, Avoided Crossing, and Resonant Excitation in Kerr Quasinormal Modes near Algebraically Special Frequencies
- Quasinormal modes of rotating black holes with ultralight scalar fields
- Quasinormal modes of Kerr-Newman-de Sitter black holes from Heun connection formulae
BibTeX
@misc{iida2026parameter,
title = {{Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder}},
author = {Momoka Iida and Hayato Motohashi and Hirotaka Takahashi},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.14277},
eprint = {2609.14277},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.14277},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder
AU - Momoka Iida
AU - Hayato Motohashi
AU - Hirotaka Takahashi
PY - 2026
DA - 2026/09/13
PB - arXiv
DO - 10.48550/arxiv.2609.14277
UR - https://arxiv.org/abs/2609.14277
AB - Ringdown gravitational waves from binary black hole mergers can be modeled as superpositions of quasinormal modes (QNMs), whose frequencies and excitation factors encode properties of the remnant Kerr black hole. Reliable extraction of multiple QNM components is challenging because of mode overlap and noise. We develop an autoencoder-based framework for multi-component QNM analysis, in which the latent space is trained to represent the physical parameters of individual modes, enabling waveform denoising and parameter estimation within a common framework. Using controlled model waveforms constructed as finite sums of Kerr QNMs with recently established high-precision frequencies and excitation factors, including their nontrivial spin dependence near resonant excitation, we assess the method across partitioned spin intervals. The model achieves good in-domain waveform reconstruction and parameter recovery for the two longest-lived components of eight-component input waveforms, while its performance degrades when the validation spins lie far outside the training range. In a selected spin interval, the framework also recovers the 32 parameters of an eight-component waveform with good overall agreement. These results demonstrate the feasibility of physics-informed autoencoder-based inference for a prescribed multi-component ringdown waveform family and motivate further tests with progressively more realistic signals.
ER -