Inverse-problem versus principal component analysis methods for angular differential imaging of circumstellar disks. The mustard algorithm

TL;DR

The paper introduces MUSTARD, a new inverse problem algorithm that enhances the recovery of rotation-invariant circumstellar disk features in high-contrast imaging, outperforming PCA methods under good conditions.

Contribution

MUSTARD incorporates morphological priors into inverse problem approaches to improve disk image reconstruction from ADI data, addressing rotation invariance issues.

Findings

MUSTARD significantly improves rotation-invariant signal recovery in good conditions.

MUSTARD performs less effectively on unstable ADI data sets.

MUSTARD provides shallower detection limits compared to PCA-based methods.

Abstract

Circumstellar disk images have highlighted a wide variety of morphological features. Recovering disk images from high-contrast angular differential imaging (ADI) sequences are however generally affected by geometrical biases, leading to unreliable inference of the morphology of extended disk features. Recently, two types of approaches have been proposed to recover more robust disk images from ADI sequences: iterative principal component analysis, and inverse problem approaches. We introduce MUSTARD, a new IP-based algorithm designed to address the problem of the flux invariant to the rotation in ADI sequences; a limitation inherent to the ADI observing strategy, and discuss the advantages of IP approaches with respect to PCA-based algorithms. The MUSTARD model relies on the addition of morphological priors on the disk and speckle field to a standard IP approach to tackle rotation-invariant signal in circumstellar disk images. We compare the performance of MUSTARD, I-PCA, and standard PCA on a sample of high-contrast imaging data sets acquired in different observing conditions, after injecting a variety of synthetic disk models at different contrast levels. MUSTARD significantly improves the recovery of rotation-invariant signal in disk images, especially for data sets obtained in good observing conditions. However, the MUSTARD model is shown to inadequately handle unstable ADI data sets, and to provide shallower detection limits than PCA-based approaches. MUSTARD has the potential to deliver more robust disk images by introducing a prior to address the inherent ambiguity of ADI observations. However, the effectiveness of the prior is partly hindered by our limited knowledge of the morphological and temporal properties of the stellar speckle halo. In light of this limitation, we suggest that the algorithm could be improved by enforcing a prior based on a library of reference stars