Compressed sensing imaging techniques for radio interferometry

TL;DR

This paper introduces convex optimization-based compressed sensing imaging techniques for radio interferometry, improving reconstruction accuracy for astrophysical signals from incomplete Fourier measurements.

Contribution

It presents a new generic framework for radio interferometric imaging using convex optimization, allowing incorporation of prior information for better reconstructions.

Findings

Enhanced reconstruction of astrophysical signals in simulations

Comparison shows improvements over standard algorithms

Framework applicable to various signal types

Abstract

Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements. The theory of compressed sensing demonstrates that such measurements may actually suffice for accurate reconstruction of sparse or compressible signals. We propose new generic imaging techniques based on convex optimization for global minimization problems defined in this context. The versatility of the framework notably allows introduction of specific prior information on the signals, which offers the possibility of significant improvements of reconstruction relative to the standard local matching pursuit algorithm CLEAN used in radio astronomy. We illustrate the potential of the approach by studying reconstruction performances on simulations of two different kinds of signals observed with very generic interferometric configurations. The first kind is an intensity field of compact astrophysical objects. The second kind is the imprint of cosmic strings in the temperature field of the cosmic microwave background radiation, of particular interest for cosmology.