Welcome to the Ariel Machine Learning Data Challenge. The Ariel Space mission is a European Space Agency mission to be launched in 2028. Ariel will observe the atmospheres of 1000 extrasolar planets - planets around other stars - to determine how they are made, how they evolve and how to put our own Solar System in the gallactic context.
Space mission data analysis is not easy. Especially if you need to observe a planet passing in front of its star that is often 100s of lightyears away. At such distance, one of the main issues is differentiating what is planet, what is star and what is the instrument. In this challenge we try to identify and correct for the effects of spots on the star (aptly called star-spots) from the faint signals of the exoplanets' atmospheres in the presence of signal distortions by the instrument. This is a data challenge that cannot be solved by conventional astrophysics methods, hence a machine learning data challange is in order! We provide more background in the about page.
Though helping humanity to identify the next habitable Earth should be enough, there's also a prize!. We will pay for the registration fees for the two top-ranked participants to go to ECML-PKDD 2021 or an alternative €500 cash prize. For more information on the rules and the setup, have a look at the documentation pages.
ECML-PKDD for hosting the data challenge and to the Europlanet Society for awarding the prize. We are grateful to the UK Space Agency and the European Research Council for support this effort. Furthermore, thanks to CEA Paris-Saclay, Sapienza University and UCL for providing essential scientific consultancy. Also many thanks to the data challenge team, see here for some info on the team members, and of course thanks to the Ariel team for technical support and building the space mission in the first place!
Any questions or something gone wrong? Contact us at: exoai.ucl [at] gmail.com