Myopic exoplanet detection algorithm based on an analytical model of AO-corrected coronagraphic multi-spectral imaging.
Marie Ygouf (1,2,3) - Laurent M. Mugnier (1,3) - David Mouillet (2,3)- Thierry Fusco (1,3) - Jean-Luc Beuzit (2,3)
(1) ONERA (2) IPAG (3) GIS PHASE
High contrast imaging for the detection and characterisation of exoplanets rests upon the instrument’s capability to cancel the light of the host star. Unfortunately the combination of adaptive optics and coronagraphy is not sufficient: the residual starlight, or speckle noise, may be relatively bright compared to the signal of the planet and limits the detection sensitivity. These speckles find their origin in wavefront errors created by imperfections in the optical components. As they evolve on various time scales, calibrating these speckles out is very tricky and the suppression of the unavoidable residual speckle noise must be done by post-processing methods. The current empirical post-processing methods for calibrating out the residual speckles and detecting the potential exoplanets are not sufficient with respect to the specifications to be reached by the new and future generations of instruments. In this communication, we develop, in a bayesian framework, an inversion method that is based on an analytical imaging model. The model links the instrumental aberrations to the speckle pattern on the image focal plane, distinguishing between aberrations upstream and downstream of the coronagraph. This approach allows us to estimate both the speckles and the object map using the fact that the object does not scale with the wavelength as the speckle pattern does. We validate this method on realistic images with simulation conditions typical of a SPHERE-like instrument. We assess the performance of the method for different contrasts between the star and the planet flux.