Interferometric radio transient reconstruction in compressed sensing framework

TL;DR

This paper presents a novel compressed sensing-based interferometric radio transient imaging method that effectively separates and reconstructs transient sources in space and time using wavelet dictionaries.

Contribution

It introduces a new 2D-1D reconstruction approach with independent spatial and temporal wavelet dictionaries within the compressed sensing framework.

Findings

Effective transient detection in noisy regimes

Separable spatial and temporal reconstruction

Preliminary tests show promising results

Abstract

Imaging by aperture synthesis from interferometric data is a well-known, but is a strong ill-posed inverse problem. Strong and faint radio sources can be imaged unambiguously using time and frequency integration to gather more Fourier samples of the sky. However, these imagers assumes a steady sky and the complexity of the problem increases when transients radio sources are also present in the data. Hopefully, in the context of transient imaging, the spatial and temporal information are separable which enable extension of an imager fit for a steady sky. We introduce independent spatial and temporal wavelet dictionaries to sparsely represent the transient in both spatial domain and temporal domain. These dictionaries intervenes in a new reconstruction method developed in the Compressed Sensing (CS) framework and using a primal-dual splitting algorithm. According to the preliminary tests in different noise regimes, this new "Time-agile" (or 2D-1D) method seems to be efficient in detecting and reconstructing the transients temporal dependence.