Two main types of activities have been performed over the course of the project: (i) fundamental development of techniques and methods to improve high-contrast imaging, and (ii) application of these techniques and methods to instrumental developments and data analysis.
In the first category, we have developed a new algorithm, referred to as the "regime-switching model detection map" (RSM), to process high-contrast imaging data sets. RSM uses advanced statistical tools originally developed in the field of economics to analyse time series. In parallel, we proposed new metrics to compare the performance of various image processing algorithms, and organised a community-wide exoplanet imaging data challenge in an attempt to rank the merits of various algorithms proposed over the last 10+ years in the high-contrast imaging community. Our RSM map provided some of the best results among about thirty entries. On the contrary, our first machine-learning algorithm based on a convolutional neural network (CNN) showed significant performance limitations, which we addressed with follow-up developments. Besides image processing, the work also focused on the development of new focal-plane wavefront sensing (FP-WFS) techniques for high-contrast imaging instruments. We reformulated the problem of FP-WFS as a supervised machine learning problem, and trained a series state-of-the-art CNNs to perform this task. This analysis shows that CNNs provide robust FP-WFS capabilities, and performance compatible with fundamental limits. Finally, we started to explore the design of new types of vortex coronagraphs in an attempt to enable better starlight cancellation, using the concept of optical metasurfaces.
In the second category, our efforts focused on the detailed design ("Phase C") of the METIS instrument. Our main goal was to finalise the detailed design of the METIS coronagraphs and of its high-contrast imaging modes, and to define the detailed strategy that will be used to optimise the instrument performance. We put a significant efforts in the adaptation and practical implementation of FP-WFS techniques to the case of METIS, using our machine learning approach. These activities are largely based on the end-to-end simulation software that we specifically developed for METIS, and which also provides a means to predict the yield of METIS in terms of planet detection. Besides METIS, we also provided a strong contribution to an early demonstration of the ground-based high-contrast imaging in the mid-infrared though the NEAR project at the Very Large Telescope. Our contribution consisted of the delivery of a new coronagraph and of a dedicated pointing control algorithm, which we helped install and test at the telescope. We contributed to the 100-h observing campaign on alpha Centauri with VLT/NEAR, and to the subsequent data analysis. This project led to the first candidate detection of a planet smaller than the mass of Jupiter, possibly down to the mass of Neptune. Besides our contribution to VLT/NEAR observing campaign, we participated in a series of other observing programs in collaboration with several other teams, including the early scientific exploitation of the vortex coronagraph that we built and commissioned for the ERIS camera at the VLT.
