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Angular and spectral differential imaging is an observational technique ofchoice to investigate the immediate vicinity of stars. The relative angularmotion and spectral scaling between on-axis and off-axis sources are exploitedby post-processing techniques to separate two components: the residual starlight and the light coming from surrounding objects such as circumstellar disksor point-like objects. This paper introduces a new algorithm to jointly unmixthese two components and deconvolve disk images. The proposed algorithm isbased on a statistical modeling of the residual star light, accounting for itsspatial and spectral correlations. While critical, these correlations are notmodeled and compensated for by existing reconstruction algorithms. We leveragededicated shrinkage techniques to estimate the large number of parameters ofour model of correlations in a data-driven fashion. We show that the resultingseparable model of the spatial and spectral covariances captures veryaccurately the star light, thus allowing its efficient suppression. We applyour method on several datasets from the VLT/SPHERE instrument and compare theperformances against standard algorithms of the field (cADI, PCA). We show thataccounting for the multiple correlations of the data significantly enhances thereconstruction quality, leading to better preservation of both the diskmorphology and photometry. Thanks to its unique joint spectral modeling of thedata, 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-axisobjects.

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