Kalman-smoother imputation of data gaps in simulated LISA/Taiji-style observations removes the mass-ratio and spin biases caused by windowing, at lower cost than noise inpainting.
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A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors
Kalman-smoother imputation of data gaps in simulated LISA/Taiji-style observations removes the mass-ratio and spin biases caused by windowing, at lower cost than noise inpainting.