Ariel Data Challenge Series launched in 2019 to build global community for exoplanet data solutions

Ariel, has launched a global competition series to find innovative solutions for the interpretation and analysis of exoplanet data. The first Ariel Data Challenge invited professional and amateur data scientists around the world to use Machine Learning (ML) to remove noise from exoplanet observations caused by star-spots and by instrumentation. The Ariel ML contest has been selected as a Discovery Challenge by the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). Over 100 international teams participated to the challenge. The winners were awarded at ECMLPKDD and EPSC-DPS 2019.

Results have been published in Nikolaou N. et al. Lessons Learned from the 1st Ariel Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots

Alt Image credit ESA/STFC RAL Space/UCL/UK Space Agency