REXPACO ASDI: Joint unmixing and deconvolution of the circumstellar environment by angular and spectral differential imaging

TL;DR

This paper presents a novel joint unmixing and deconvolution algorithm for circumstellar imaging that models spatial and spectral correlations to improve the separation of star light from surrounding objects, enhancing image quality.

Contribution

The paper introduces a new statistical modeling approach with shrinkage techniques for joint unmixing and deconvolution in angular and spectral differential imaging, outperforming standard methods.

Findings

Enhanced disk reconstruction quality compared to cADI and PCA.

Ability to detect disks at intensities one million times fainter than the star.

Effective suppression of residual star light through correlation modeling.

Abstract

Angular and spectral differential imaging is an observational technique of choice to investigate the immediate vicinity of stars. The relative angular motion and spectral scaling between on-axis and off-axis sources are exploited by post-processing techniques to separate two components: the residual star light and the light coming from surrounding objects such as circumstellar disks or point-like objects. This paper introduces a new algorithm to jointly unmix these two components and deconvolve disk images. The proposed algorithm is based on a statistical modeling of the residual star light, accounting for its spatial and spectral correlations. While critical, these correlations are not modeled and compensated for by existing reconstruction algorithms. We leverage dedicated shrinkage techniques to estimate the large number of parameters of our model of correlations in a data-driven fashion. We show that the resulting separable model of the spatial and spectral covariances captures very accurately the star light, thus allowing its efficient suppression. We apply our method on several datasets from the VLT/SPHERE instrument and compare the performances against standard algorithms of the field (cADI, PCA). We show that accounting for the multiple correlations of the data significantly enhances the reconstruction quality, leading to better preservation of both the disk morphology and photometry. Thanks to its unique joint spectral modeling of the data, the proposed algorithm can reconstruct disks having a circular symmetry (e.g., rings, spirals) at intensities one million times fainter than the star, without resorting to additional reference datasets free from the off-axis objects.