The Atmospheric Remote-Sensing Infrared Exoplanet Large-survey, ARIEL, has been selected to be the next M4 space mission in the ESA Cosmic Vision programme. From launch in 2028, and during the following 4 years of operation, ARIEL will perform precise spectroscopy of the atmospheres of about 1000 known transiting exoplanets using its metre-class telescope, a three-band photometer and three spectrometers that will cover the 0.5 µm to 7.8 µm region of the electromagnetic spectrum. The payload is designed to perform primary and secondary transit spectroscopy, and to measure spectrally resolved phase curves with a stability of < 100 ppm (goal 10 ppm). Observing from an L2 orbit, ARIEL will provide the first statistically significant spectroscopic survey of hot and warm planets. These are an ideal laboratory in which to study the chemistry, the formation and the evolution processes of exoplanets, to constrain the thermodynamics, composition and structure of their atmospheres, and to investigate the properties of the clouds.
The Atmospheric Remote-Sensing Infrared Exoplanet Large-survey (ARIEL) is one of the three candidate missions selected by the European Space Agency (ESA) for its next medium-class science mission due for launch in 2026. The goal of the ARIEL mission is to investigate the atmospheres of several hundred planets orbiting distant stars in order to address the fundamental questions on how planetary systems form and evolve.
During its four (with a potential extension to six) years mission ARIEL will observe 500+ exoplanets in the visible and the infrared with its meter-class telescope in L2. ARIEL targets will include gaseous and rocky planets down to the Earth-size around different types of stars. The main focus of the mission will be on hot and warm planets orbiting close to their star, as they represent a natural laboratory in which to study the chemistry and formation of exoplanets.
The ARIEL mission concept has been developed by a consortium of more than 50 institutes from 12 countries, which include UK, France, Italy, Germany, the Netherlands, Poland, Spain, Belgium, Austria, Denmark, Ireland and Portugal. The analysis of the ARIEL spectra and photometric data in the 0.5-7.8 micron range will allow to extract the chemical fingerprints of gases and condensates in the planets’ atmospheres, including the elemental composition for the most favorable targets. It will also enable the study of thermal and scattering properties of the atmosphere as the planet orbit around the star.
ARIEL will have an open data policy, enabling rapid access by the general community to the high-quality exoplanet spectra that the core survey will deliver.
The high sensitivity and broad wavelength coverage of the James Webb Space Telescope will transform the field of exoplanet transit spectroscopy. Transit spectra are inferred from minute, wavelength-dependent variations in the depth of a transit or eclipse as the planet passes in front of or is obscured by its star, and the spectra contain information about the composition, structure and cloudiness of exoplanet atmospheres. Atmospheric retrieval is the preferred technique for extracting information from these spectra, but the process can be confused by astrophysical and instrumental systematic noise.
We present results of retrieval tests based on synthetic, noisy JWST spectra, for clear and cloudy planets and active and inactive stars. We find that the ability to correct for stellar activity is likely to be a limiting factor for cloudy planets, as the effects of unocculted star spots may mimic the presence of a scattering slope due to clouds.
We discuss the pros and cons of the available JWST instrument combinations for transit spectroscopy, and consider the effect of clouds and aerosols on the spectra. Aerosol high in a planet’s atmosphere obscures molecular absorption features in transmission, reducing the information content of spectra in wavelength regions where the cloud is optically thick. We discuss the usefulness of particular wavelength regions for identifying the presence of cloud, and suggest strategies for solving the highly-degenerate retrieval problem for these objects.