Welcome to the Ariel Machine Learning Data Challenge. The Ariel Space mission is a European Space Agency mission to be launched in 2029. 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.

• 29-Jul - Test Data Documentation released
• 29-Jul - Timeline announced, see below.
• 18-Jul - Light Track scoring metric updated - Upload your solution to get the latest score!
• 15-Jul - New Slack channel for discussions and Q&A.
• 15-Jul - Full release of the baseline model and scoring metric on our GitHub repository

### Timeline

• 30-Jun - Challenge begins
• 30-Jun - Baseline solution and other documentations released
• 30-Jun - Challenge is live!
• 30-Jun - 1st release of test data
• 01-Sept - 2nd release of test data, Scores on leaderboard will reset
• 17-Oct - Invitation to Final Evaluation round
• 17-Oct to 21-Oct - Final Evalution window (please note late submissions will not be accepted)
• 21-Oct to 04-Nov - Evaluation period
• 04-Nov to 08-Nov - Winners are informed & announced
• 28 Nov (nominal) - Winning solutions presented at NeurIPS 2022

### Understanding worlds in our Milky Way

Today we know of roughly 5000 exoplanets in our Milky Way galaxy. Given that the first planet was only conclusively discovered in the mid-1990's, this is an impressive achievement. Yet, simple number counting does not tell us much about the nature of these worlds. One of the best ways to understand their formation and evolution histories is to understand the composition of their atmospheres. What's the chemistry, temperatures, cloud coverage, etc? Can we see signs of possible bio-markers in the smaller Earth and super-Earth planets? Since we can't get in-situ measurements (even the closest exoplanet is lightyears away), we rely on remote sensing and interpreting the stellar light that shines through the atmosphere of these planets. Model fitting these atmospheric exoplanet spectra is tricky and requires significant computational time. This is where you can help!

### Help us to speed up our model fitting!

Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation.

### There are prizes

The first prizes for light and regular tracks are $1000 and$2000 respectively. The second prizes are \$500 each. The first prize winners will also be invited to attend our NeurIPS workshop.

#### Many thanks to...

NeurIPS 2022 for hosting the data challenge and to the UK Space Agency and the European Research Council for support this effort. Also many thanks to the data challenge team and partnering institutes, 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