Status of the MEDUSAE post-processing method to detect circumstellar objects in high-contrast multispectral images

TL;DR

The paper reviews the current status of the MEDUSAE method, which detects exoplanets and disk features in multispectral high-contrast images, highlighting the need for more realistic PSF modeling for improved real data application.

Contribution

This work assesses MEDUSAE's performance on realistic simulations and emphasizes the importance of refining the coronagraphic PSF model for future real data use.

Findings

MEDUSAE successfully estimates speckle fields and object maps in simulated data.

Current model limitations hinder performance on realistic data.

Enhanced PSF modeling is necessary for real-world applications.

Abstract

The MEDUSAE method (Multispectral Exoplanet Detection Using Simultaneous Aberration Estimation) is dedicated to the detection of exoplanets and disks features in multispectral high-contrast images. The concept of MEDUSAE is to retrieve both the speckle field and the object map via a stochastic approach to inverse problem (taking into account the statistics of the noise) in the Bayesian framework (using parametric regularization). One fundamental aspect of MEDUSAE is that the model of the coronagraphic PSF is analytic and parametrized by the optical path difference which, contrary to the phase, is achromatic. The speckle field is thus estimated by a phase retrieval, using the spectral diversity to disentangle the planetary signal from the residual starlight. The object map is restored via a non-myopic deconvolution under adequate regularization. The basis of this MEDUSAE method have been previously published and validated on an inverse crime. In this communication, we present its application to realistic simulated data, in preparation for real data application. The solution we proposed to attempt bypassing the main differences between the model used for the inversion and the real data is not sufficient: it is now necessary to make the model of the coronagraphic PSF more realistic.