Model-agnostic search of gravitational wave echoes in LVK data

Abstract

Gravitational wave echoes offer a unique probe of the near-horizon structure of astrophysical black holes, beyond the standard "black hole spectroscopy." Theoretical waveform predictions, however, remain uncertain, motivating robust searches that avoid specific echo modeling. We present a model-agnostic search framework targeting long-lived quasinormal modes (QNMs) expected from strong interior reflection. By employing a generalized phase-marginalized likelihood that coherently combines data for each QNM across a detector network, our method enhances sensitivity to the signals. To handle real detector noise, we implement an optimized notching procedure to suppress instrumental spectral lines and refine the Bayesian parameter settings. We validate the performance of this framework using injection studies on O1 background data, demonstrating reliable signal recovery in realistic noise conditions. We then apply this method to three binary black hole merger events with high ringdown signal-to-noise ratios (SNRs): GW150914 from O1, GW231226 from O4a, and the recently reported O4 event GW250114. No statistically significant evidence for postmerger echoes is found. Consequently, we derive 90% upper limits on the network SNR and the average initial strain amplitude of the long-lived QNMs. These results provide model-agnostic constraints on late-time echoes from LVK data, complementing existing searches for other echo signatures.