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The ESA-Ariel mission will include a tier dedicated to exoplanet phase curves corresponding to ~10% of the science time. We present here the current observing strategy for studying exoplanet phase curves with Ariel. We define science questions, requirements and a list of potential targets. We also estimate the precision of phase curve reconstruction and atmospheric retrieval using simulated phase curves. Based on this work, we found that full-orbit phase variations for 35-40 exoplanets could be observed during the 3.5-yr mission. This statistical sample would provide key constraints on atmospheric dynamics, composition, thermal structure and clouds of warm exoplanets, complementary to the scientific yield from spectroscopic transits/eclipses measurements.
Launching in 2028, ESAs Atmospheric Remote-sensing Exoplanet Large-survey (ARIEL) survey of $sim$1000 transiting exoplanets will build on the legacies of Kepler and TESS and complement JWST by placing its high precision exoplanet observations into a
The occurrence of a planet transiting in front of its host star offers the opportunity to observe the planets atmosphere filtering starlight. The fraction of occulted stellar flux is roughly proportional to the optically thick area of the planet, the
We present a new matched filter algorithm for direct detection of point sources in the immediate vicinity of bright stars. The stellar Point Spread Function (PSF) is first subtracted using a Karhunen-Loeve Image Processing (KLIP) algorithm with Angul
Ariel has been selected as ESAs M4 mission for launch in 2028 and is designed for the characterisation of a large and diverse population of exoplanetary atmospheres to provide insights into planetary formation and evolution within our Galaxy. Here we
Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that Convolutional Neural Networks (CNN) provide an efficient solution to