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The last decade has witnessed a rapid growth of the field of exoplanetdiscovery and characterisation. However, several big challenges remain, many ofwhich could be addressed using machine learning methodology. For instance, themost prolific method for detecting exoplanets and inferring several of theircharacteristics, transit photometry, is very sensitive to the presence ofstellar spots. The current practice in the literature is to identify theeffects of spots visually and correct for them manually or discard the affecteddata. This paper explores a first step towards fully automating the efficientand precise derivation of transit depths from transit light curves in thepresence of stellar spots. The methods and results we present were obtained inthe context of the 1st Machine Learning Challenge organized for the EuropeanSpace Agency's upcoming Ariel mission. We first present the problem, thesimulated Ariel-like data and outline the Challenge while identifying bestpractices for organizing similar challenges in the future. Finally, we presentthe solutions obtained by the top-5 winning teams, provide their code anddiscuss their implications. Successful solutions either construct highlynon-linear (w.r.t. the raw data) models with minimal preprocessing -deep neuralnetworks and ensemble methods- or amount to obtaining meaningful statisticsfrom the light curves, constructing linear models on which yields comparablygood predictive performance.

Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots