SearcharxivSearch

arXiv subjects

Motoki Suzuki

Publications and source records attributed to Motoki Suzuki.

2 recordsLinked to original sources

Ringdown analysis of GW250114 with orthonormal modes

GW250114 is the loudest gravitational wave event to date observed by LIGO-Virgo-KAGRA Collaboration. Owing to its high signal-to-noise ratio (SNR), previous analyses based on quasinormal mode (QNM) superpositions have suggested evidence of the fundamental and the first overtone of the $\ell=m=2$ mode in this event. However, QNMs are not orthogonal and the inclusion of multiple QNMs induces correlations among them, which can hinder the robust identification of subdominant QNMs. To address this challenge, we apply an analysis based on orthonormalized QNMs [S. Morisaki $\textit{et al.}$, Phys. Rev. D $\textbf{112}$, 124083 (2025)] to GW250114. We find that, in the model including three $\ell=m=2$ QNMs up to the second overtone, the first overtone of the $\ell=m=2$ mode is more strongly supported than in previous nonorthogonal analyses, with the inferred significance increasing from $82.5\%$ to $99.9\%$. Furthermore, we estimate deviations from the Kerr prediction using the orthonormal QNM framework and find no significant deviation, consistent with previous analyses. These results demonstrate that the orthonormal QNM framework provides a more robust way to identify subdominant modes in high-SNR ringdown signals, highlighting its potential for future gravitational wave observations.

gr-qc

Analyzing black-hole ringdowns with orthonormal modes

The ringdown signal following a black hole (BH) merger can be modeled as a superposition of BH quasinormal modes (QNMs), offering a clean setup for testing gravitational theories. In particular, detecting multiple QNMs enables consistency checks of their frequencies and damping times, serving as a test of general relativity -- a technique known as black hole spectroscopy. However, incorporating additional QNMs introduces challenges such as increased parameter correlations and higher computational costs in data analysis. To address this, we propose an efficient Bayesian analysis method that applies the Gram-Schmidt algorithm to the QNMs. This reduces the correlation between the modes and enables analytic marginalization over the mode amplitudes. We validate our approach using damped sinusoids and numerical waveforms from the Simulating eXtreme Spacetimes catalog.

gr-qc