The Ariel mission is an European Space Agency (ESA) mission, planned to fly in 2029.
Ariel will address the fundamental questions on what exoplanets are made of and how planetary systems form and evolve by investigating the atmospheres of many hundreds of diverse planets orbiting different types of stars. Ariel will observe around a thousand transiting planets, including gas giants, Neptunes, super-Earths and Earth- size planets around a range of host star types. This large and unbiased survey will contribute to answering the first of the four ambitious topics listed in the ESA’s Cosmic Vision:
“What are the conditions for planet formation and the emergence of life?”
The ultimate goal of the Ariel mission is to understand the physical processes behind both planetary formation and atmospheric evolution for the different classes of planets (Turrini et al, 2021; Tinetti et al 2021).
More specifically, Ariel will reveal chemical fingerprints of gases and condensates in the planetary atmospheres, including their thermal structure and elemental composition, enabling the investigation of all corners of the exoplanet population, from temperate terrestrial to ultra-hot Jupiters. Meeting such an objective requires performing a population study with a homogeneous approach, to allow for a direct comparison between all planetary systems that will be observed. In particular, for correctly interpreting the atmospheric data that Ariel will retrieve, each host star in the sample must be characterised with high precision in a uniform way (Danielski et al, 2022).
Ariel website : https://arielmission.space/
ESA Ariel website: https://www.esa.int/Science_Exploration/Space_Science/Ariel
To optimise the science outcome of Ariel it is pivotal to know the fundamental properties of the host stars in the Ariel Mission Candidate Sample, their chemistry, their level of activity, their orbital properties, their galactic origins, their age.
Our goal is to provide an accurate knowledge of such properties, well before the mission launch in 2029. This is essential to determine the final target list to be observed with Ariel.
The characterisation of planet-hosting star(s) is an important step for understanding the nature of their transiting planetary companion(s). On a general level, characterising the star is salient for the following reasons :
The determination of the planetary radius, mass, and bulk composition -- they are directly dependent by the accurate and precise determination of the stellar radius and mass. In turn, the inference of both stellar radius and mass usually hinges on the stellar atmospheric parameters through the use of stellar models.
The exact identification of atmospheric features -- stellar variability, caused by the interaction between magnetic fields and turbulent plasma, is responsible for intrinsic variations of the stellar spectrum that can be confused with planetary features when occurring on transit (or phase-curve) timescales. It is therefore essential to acquire an exact knowledge of the stellar spectrum to at least the same level of the planetary signal, for accurately determining the chemical composition and molecular abundances in a planetary atmosphere. Furthermore, information on long-term stellar activity is important for studying the effect of the stellar irradiation onto the planet atmosphere and for constraining the evolution of the atmosphere itself.
The investigation of planetary formation and migration processes -- determining the precise stellar age and, by extension, of the planetary system is critical as it tells us how much time was available for the system to dynamically evolve. This, in turn, allows us to constrain the pathways that produced its current architecture. Knowing the age of the system provides the temporal dimension of the problem. In parallel, the atmospheric composition of the host star is itself a cipher key to decode the compositional signatures on planets left by their formation and migration histories."
The knowledge of the galactic origin of the planetary systems -- protoplanetary discs formed in different galactic environments will present different properties, such as amount of refractory vs volatiles, or the level of irradiation. A disc located in a overdensity region would suffer higher stellar irradiation, and hence could present different chemical properties than a disc in a field region. Furthermore, their dissipation timescales will be very different, hinging for instance the formation of gas giants in the first case. Knowing the stellar kinematics, and hence where in the Galaxy the planetary systems formed, is extremely important to understand trends and correlations found between stellar and planetary properties, as well as having contraints on the planetary classes occurrence rates.
For Ariel the concept of homogeneity is crucial: fundamental parameters of host stars are usually found in the literature as the result of a case-by-case analysis performed by different teams, resulting in an inhomogeneous census of stars with planets.
In order to push the state-of-the-art of the field forward, and prepare the ground for the success of the Ariel, the stellar parameters need to be homogeneous and self-consistent. A sample of uniform and self-consistent stellar parameters will establish a robust reference frame that will enable us to perform comparative planetary studies for the thousand planets, and hence to shed light on their formation and evolution on a global scale. "
Our approach accounts for the concept of homogeneity, but also the concept of coherence among a large set of stellar parameters. Beginning from the measurement of a robust set of atmospheric parameters, we use those to determine all the others parameters, so that Ariel stars will be self-consistent for studies within the Consortium, but also for the general community.
Furthermore, our approach also accounts for the diversity of the methods employed. Given the incoherence seen in the literature between results obtained with different techniques (e.g. empirical vs model based) and/or by different teams, it is necessary to quantify and understand the reasons behind these discrepancies, so that they can be accounted for in the data interpretation.
The extended methodology can be found in Danielski et al., 2022.