The Ariel Machine Learning Data Challenge

The use of machine and deep learning is prevalent in many fields of science and industry and is now becoming more widespread in extrasolar planet and solar system sciences. Deep learning holds many potential advantages when it comes to modelling highly non-linear data, as well as speed improvements when compared to traditional analysis and modelling techniques.

One such problem is the identification and de-trending of stellar and systematic instrument noise in exoplanet lightcurves and in particular time-resolved spectroscopy of exoplanet atmospheres.

As part of the ESA Ariel Space mission and the European Conference on Machine Learning (ECML-PKDD), we have organised two very successful machine learning challenges in 2019 and 2021 (https://www.ariel-datachallenge.space).  The aim was to provide new solutions to traditionally intractable problems and to foster closer collaboration between the exoplanet and machine learning communities. Often interdisciplinary approaches to long-standing problems are thwarted by jargon and a lack of familiarity. Data challenges are an excellent way to break down existing barriers and establish new links and collaborations.

The top-ranked approaches range from deep-learning to gradient boosted extreme learning machines. In this presentation, I will discuss their pros and cons of solving the set challenge and show how new ideas generated by the data challenges can achieve real progress in the field.