Parameter Estimation with Nonstationary Noise in Gravitational-wave Data

Abstract: The sensitivity of gravitational-wave (GW) detectors is characterized by their noise curves, which determine the detector's reach and ability to measure the parameters of astrophysical sources accurately. The detector noise is typically modeled as stationary and Gaussian for many practical purposes and is characterized by its Power Spectral Density (PSD). However, due to environmental and instrumental factors, physical changes in the state of detectors may introduce non-stationarity into the noise. Misestimation of the noise behavior directly impacts the posterior width of the signal parameters. It becomes an issue for studies that depend on accurate localization volumes, such as i) probing cosmological parameters (e.g., Hubble constant) using cross-correlation methods with galaxies, ii) doing electromagnetic follow-up using localization information from parameter estimation (PE) done from pre-merger data. We study the effects of dynamical noise on the PE of the GW events. We develop a new method to correct dynamical noise by estimating a locally valid pseudo-PSD normalized along a potential signal's time-frequency track. We do simulations by injecting binary neutron star (BNS) merger signals in various scenarios where the detector goes through a period of non-stationarity with reference noise curves of third-generation detectors (Cosmic Explorer, Einstein telescope). As an example, for a source where mis-modeling of the noise biases the signal-to-noise estimate by even $10\%$, one would expect the estimated sky localization to be either under or over-reported by $\sim 20\%$; errors like this, especially in low-latency, could potentially cause follow-up campaigns to miss the actual source location.