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Exploring the no-hair theorem with LISA

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arxiv 2406.14552 v2 pith:LLSV5GN3 submitted 2024-06-20 gr-qc

classification gr-qc
keywords deviationsestimatedlisaanalysismodeparametersapproachapproaches
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abstract

In this study, we explore the possibility of testing the no-hair theorem with gravitational waves from massive black hole binaries in the frequency band of the Laser Interferometer Space Antenna (LISA). Based on its sensitivity, we consider LISA's ability to detect possible deviations from general relativity (GR) in the ringdown. Two approaches are considered: an agnostic quasi-normal mode (QNM) analysis, and a method explicitly targeting the deviations from GR for given QNMs. Both approaches allow us to find fractional deviations from general relativity as estimated parameters or by comparing the mass and spin estimated from different QNMs. However, depending on whether we rely on the prior knowledge of the source parameters from a pre-merger or inspiral-merger-ringdown (IMR) analysis, the estimated deviations may vary. Under some assumptions, the second approach targeting fractional deviations from GR allows us to recover the injected values with high accuracy and precision. We obtain $(5\%, 10\%)$ uncertainty on ($\delta \omega, \delta \tau)$ for the $(3,3,0)$ mode, and $(3\%, 17\%)$ for the $(4,4,0)$ mode. As each approach constrains different features, we conclude that combining both methods would be necessary to perform a better test. In this analysis, we also forecast the precision of the estimated deviation parameters for sources throughout the mass and distance ranges observable by LISA.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Nonlinearities in Kerr Black Hole Ringdown from the Penrose Limit

    gr-qc 2025-07 conditional novelty 7.0 of 10

    Kerr quadratic quasi-normal mode amplitudes and phases are computed analytically in the eikonal limit via the Penrose limit, giving an explicit spin-dependent nonlinearity ratio.

  2. A pipeline for searching and fitting instrumental glitches in LISA data

    gr-qc 2025-05 conditional novelty 6.0 of 10

    A reversible-jump MCMC pipeline simultaneously fits LISA instrumental glitches, noise, and a massive black hole binary signal, validated on simulated and modified Spritz challenge data.

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