Machine Learning Data Challenge

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. You can find our press release here.

Machine vs Star Spots

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 and what is star. 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. 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.

The Prize!

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 2019 in W├╝rzburg. For more information on the rules and the setup, have a look at the documentation pages.

Closing date

The competition will close on the 15th of August 2019 with the winners announced one week later.

Many thanks to...

Special thanks go to ECML-PKDD for hosting the data challenge, to Angelos Tsiaras, Nikos Nikolaou and Mario Morvan for generating the data and providing the baseline model, to Ingo Waldmann for building the website. Also many thanks to the ExoAI team for helpful input and of course the Ariel team for technical and financial support and building the space mission in the first place!

Any questions or something gone wrong? Contact us at: exoai.ucl [at]

Ariel h2020 UKSA UCL