The WDM time-frequency transform in gravitational-wave data analysis II: Evolutionary spectrum and noise covariance

Noise cannot be treated as stationary over the timescales required for upcoming gravitational-wave detectors, minutes for ground-based and months for space-based detectors. Nonstationarity leads to a non-diagonal frequency-domain noise covariance matrix, thus motivating a shift to the time-frequency domain. We introduce evolutionary power spectra for describing nonstationary noise in the Wilson-Daubechies-Meyer time-frequency basis. Under the framework of oscillatory processes, we show that, to leading order in the ratio of the Meyer filter and the nonstationarity timescales, the evolutionary spectrum is a power-frequency distribution that can be estimated from a single noise realization. We derive the corresponding noise covariance matrix and show that it is band tri-diagonal, like its stationary counterpart. The exact covariance to all orders is positive semi-definite by construction and dense depending on the noise. We further derive the optimal tiling of the time-frequency plane that minimizes the error of a diagonal covariance approximation and show that coarse-graining the plane suppresses correlations quadratically. Using simulated data with a LISA Galactic foreground and a drifting powerlaw spectrum, we show that the stationarity approximation leads to signal-to-noise ratio loss and severely biases inference. For realistic LISA data, approximating the covariance as diagonal in the Wilson-Daubechies-Meyer domain is accurate for the narrow-band analysis of a single resolved Galactic binary, but insufficient for a broad-band analysis. Finally, we provide mitigation strategies for the wraparound correlations induced across the data time edges whenever the spectrum differs between the start and the end of the data.