Compressed sensing reconstruction of a string signal from
  interferometric observations of the cosmic microwave background
  We propose an algorithm for the reconstruction of the signal induced by
cosmic strings in the cosmic microwave background (CMB), from
radio-interferometric data at arcminute resolution. Radio interferometry
provides incomplete and noisy Fourier measurements of the string signal, which
exhibits sparse or compressible magnitude of the gradient due to the
Kaiser-Stebbins (KS) effect. In this context the versatile framework of
compressed sensing naturally applies for solving the corresponding inverse
problem. Our algorithm notably takes advantage of a model of the prior
statistical distribution of the signal fitted on the basis of realistic
simulations. Enhanced performance relative to the standard CLEAN algorithm is
demonstrated by simulated observations under noise conditions including primary
and secondary CMB anisotropies.
