Large Scale 3D Image Reconstruction in Optical Interferometry

TL;DR

This paper introduces PAINTER, a scalable 3D image reconstruction algorithm for optical interferometry that effectively utilizes phase information, including differential phases, to improve multiwavelength astronomical imaging.

Contribution

The paper presents a novel iterative 3D reconstruction algorithm capable of handling large-scale polychromatic optical interferometry data, incorporating differential phase information for enhanced accuracy.

Findings

Efficient reconstruction demonstrated on synthetic data.

Incorporation of differential phases improves image quality.

Algorithm scales to large datasets.

Abstract

Astronomical optical interferometers (OI) sample the Fourier transform of the intensity distribution of a source at the observation wavelength. Because of rapid atmospheric perturbations, the phases of the complex Fourier samples (visibilities) cannot be directly exploited , and instead linear relationships between the phases are used (phase closures and differential phases). Consequently, specific image reconstruction methods have been devised in the last few decades. Modern polychromatic OI instruments are now paving the way to multiwavelength imaging. This paper presents the derivation of a spatio-spectral ("3D") image reconstruction algorithm called PAINTER (Polychromatic opticAl INTErferometric Reconstruction software). The algorithm is able to solve large scale problems. It relies on an iterative process, which alternates estimation of polychromatic images and of complex visibilities. The complex visibilities are not only estimated from squared moduli and closure phases, but also from differential phases, which help to better constrain the polychromatic reconstruction. Simulations on synthetic data illustrate the efficiency of the algorithm.