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. Read here the results: Nikolaou N. et al. Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots .
The Ariel Data Challenge series was announced in April at the UK Exoplanet Community Meeting (EXOM) 2019 in London. A second Ariel Data Challenge that focuses on the retrieval of spectra from simulations of cloudy and cloud-free super-Earth and hot-Jupiter data was also launched in April. A further data analysis challenge to create pipelines for faster, more effective processing of the raw data gathered by the mission has been launched in June at the EWASS conference in Lyon.
Outcomes from all three Ariel Data Challenges have been discussed at the ECMLPKDD in Würzburg 16-20 September 2019 and at the EPSC-DPS Joint Meeting 2019, which took place in Geneva during the same week.