Compressed sensing reconstruction of a string signal from interferometric observations of the cosmic microwave background

TL;DR

This paper introduces a compressed sensing algorithm for reconstructing cosmic string signals in the CMB from noisy radio-interferometric data, leveraging prior statistical models for improved accuracy over traditional methods.

Contribution

The paper presents a novel compressed sensing-based reconstruction algorithm that incorporates prior statistical distributions, outperforming standard techniques like CLEAN in simulated noisy conditions.

Findings

Enhanced reconstruction accuracy over CLEAN algorithm

Effective handling of noisy Fourier measurements

Utilization of realistic prior statistical models

Abstract

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.