Model-independent search for the quasinormal modes of gravitational wave echoes

TL;DR

This paper develops a model-independent Bayesian method to detect quasinormal modes in gravitational wave echoes, enabling probing of black hole near-horizon structures with reduced theoretical uncertainties.

Contribution

It introduces a phase-marginalized likelihood approach for efficient, model-independent detection of gravitational wave echo QNMs, improving robustness and sensitivity.

Findings

The method successfully detects QNMs in simulated noise.

It performs well across diverse theoretical echo models.

The approach enhances black hole seismology studies.

Abstract

Postmerger gravitational wave echoes provide a unique opportunity to probe the near-horizon structure of astrophysical black holes, which may be modified due to nonperturbative quantum gravity phenomena. However, since the waveform is subject to large theoretical uncertainties, it is necessary to develop search methods that are less reliant on specific models for detecting echoes from observational data. A promising strategy is to identify the characteristic quasinormal modes (QNMs) associated with echoes, {\it in frequency space}, which complements existing searches of quasiperiodic pulses in time. In this study, we build upon our previous work targeting these modes by incorporating relative phase information to optimize the Bayesian search algorithm. Using a new phase-marginalized likelihood, the performance can be significantly improved for well-resolved QNMs. This enables an efficient search for QNMs of various shapes, utilizing a simple search template that is independent of specific models. To demonstrate the robustness of the search algorithm, we construct four complementary benchmarks for the echo waveform that span a diverse range of different theoretical possibilities for the near-horizon structure. We then validate our Bayesian search algorithms by injecting the benchmark models into different realizations of Gaussian noise. Using two types of phase-marginalized likelihoods, we find that the search algorithm can efficiently detect the corresponding QNMs. Therefore, our search strategy provides a concrete Bayesian and model-independent approach to "quantum black hole seismology."