Radio interferometric imaging with compressive sensing
We are about to enter a new era of radio astronomy with new radio interferometric telescopes under design and construction, such as the Square Kilometre Array (SKA). While such telescopes would provide many scientific opportunities, they will also present considerable modelling and data processing challenges. Novel modelling and imaging techniques will be required to overcome these challenges. The theory of compressive sensing is a recent, revolutionary development in the field of information theory, which goes beyond the standard Nyquist-Shannon sampling theorem by exploiting the sparsity of natural images. Compressive sensing suggests a powerful framework for solving linear inverse problems (through sparse regularisation), such as recovering images from the incomplete Fourier measurements taken by radio interferometric telescopes. I will present recent developments in compressive sensing techniques for radio interferometric imaging, which have shown a great deal of promise. Furthermore, by appealing to the theoretical foundations of compressive sensing, I will discuss how telescope configurations can be optimised to further enhance imaging fidelity via the spread spectrum effect that arises in non-coplanar baseline and wide field-of-view settings. Finally, I will also present the recently released PURIFY code, an open source code for the application of these techniques to realistic radio interferometric data.