EPIC SIGNED

Project "EPIC" data sheet

The following table provides information about the project.

 Partnership

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 Project objective

One of the most ambitious goals of modern astrophysics is to characterise the physical and chemical properties of rocky planets orbiting in the habitable zone of nearby Sun-like stars. Although the observation of planetary transits could in a few limited cases be used to reach such a goal, it is widely recognised that only direct imaging techniques will enable such a feat on a statistically significant sample of planetary systems. Direct imaging of Earth-like exoplanets is however a formidable challenge due to the huge contrast and minute angular separation between such planets and their host star. The proposed EPIC project aims to enable the direct detection and characterisation of terrestrial planets located in the habitable zone of nearby stars using ground-based high-contrast imaging in the thermal infrared domain. To reach that ambitious goal, the project will focus on two main research directions: (i) the development and implementation of high-contrast imaging techniques and technologies addressing the smallest possible angular separations from bright, nearby stars, and (ii) the adaptation of state-of-the-art machine learning techniques to the problem of image processing in high-contrast imaging. While the ultimate goal of this research can likely only be reached with the advent of giant telescopes such as the Extremely Large Telescope (ELT) around 2025, the EPIC project will lay the stepping stones towards that goal and produce several high-impact results along the way, e.g. by re-assessing the occurrence rate of giant planets in direct imaging surveys at the most relevant angular separations (i.e., close to the snow line), by conducting the deepest high-contrast imaging search for rocky planets in the alpha Centauri system, by preparing the scientific exploitation of the ELT, and by providing the first open-source high-contrast image processing toolbox relying on supervised machine learning techniques.