Denoising radio interferometric images by subspace clustering
Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. Furthermore, only the end image is cleaned, reducing flexibility substantially. We propose instead a method to remove the noise explicitly before imaging. To this end, we developed an algorithm that first decomposes the sensor signals into components using Singular Spectrum Analysis and then cluster these components using graph Laplacian matrix. We show through simulation the potential for radio astronomy, in particular, illustrating the benefit for LOFAR, the low frequency array in Netherlands. From telescopic data to least-squares image estimates, far higher accuracy with low computation cost results without the need for long observation time.