Hankel low-rank matrix approximation for time series denoising in gravitational-wave science

Extracting overlapping periodic signals from noisy time-series data is a common challenge in gravitational-wave astronomy and pulsar timing. We present an approach that recasts this problem into one of low-rank matrix approximation by embedding the time series into a Hankel matrix. We demonstrate the effectiveness of this method through numerical experiments on synthetic datasets using two iterative algorithms: Cadzow and IRLS.