PAINTER: a spatio-spectral image reconstruction algorithm for optical interferometry

TL;DR

PAINTER is a novel 3D image reconstruction algorithm for optical interferometry that effectively utilizes multiwavelength data and differential phases to improve image quality despite atmospheric turbulence.

Contribution

It introduces a spatio-spectral reconstruction method that incorporates differential phases, enhancing multiwavelength imaging in optical interferometry.

Findings

The algorithm successfully reconstructs polychromatic images from synthetic data.

Inclusion of differential phases improves reconstruction accuracy.

Simulations demonstrate the method's efficiency and relevance.

Abstract

Astronomical optical interferometers sample the Fourier transform of the intensity distribution of a source at the observation wavelength. Because of rapid perturbations caused by atmospheric turbulence, the phases of the complex Fourier samples (visibilities) cannot be directly exploited. Consequently, specific image reconstruction methods have been devised in the last few decades. Modern polychromatic optical interferometric instruments are now paving the way to multiwavelength imaging. This paper is devoted to the derivation of a spatio-spectral (3D) image reconstruction algorithm, coined PAINTER (Polychromatic opticAl INTErferometric Reconstruction software). The algorithm 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 differential phases, which helps to better constrain the polychromatic reconstruction. Simulations on synthetic data illustrate the efficiency of the algorithm and in particular the relevance of injecting a differential phases model in the reconstruction.