Gravitational-Wave Inference For Noise PSD Jumps Across Data Gaps

Abstract

Long-duration gravitational-wave (GW) inference inevitably encounters data gaps, which are often accompanied by abrupt, non-stationary changes in the detector noise power spectral density (PSD). Conventional practice typically analyzes the pre-gap and post-gap data separately, causing avoidable information loss and potentially degrading parameter inference. We build on Bayesian gap augmentation in the Wilson--Daubechies--Meyer (WDM) time--frequency domain and focus on a practically important failure mode: short gaps with large PSD amplitude jumps. In this regime, the local diagonal (Whittle-like) approximation that underpins the WDM likelihood can no longer be relied upon in the time direction near the gap edges, which may lead to biased and/or less robust inference unless the local diagonal validity is restored without resorting to full segmentation. We propose a two-stage, validity-driven remedy. First, frequency-domain prior-predictive prewhitening (PW) incorporates endpoint-informed noise information to mitigate the dominant mismatch responsible for poor diagonal behavior, enabling a more favorable WDM representation while retaining computational tractability. Second, when a time-direction smoothness criterion still fails, we apply adaptive gap expanding (AGE), selectively enlarging only the minimal neighborhood around the gap boundaries needed to restore local diagonal validity. Toy-model simulations with chimeric PSD transitions show that WDM+PW+AGE yields substantially tighter and more accurate posteriors than single-sided pre-gap/post-gap analyses, while maintaining computational efficiency suitable for next-generation missions.