Thursday

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. By NAM2021, the 2021 challenge will have concluded and the winning solutions announced. I will present the most promising machine learning approaches to de-trending stellar activity from time-resolved Ariel spectroscopy from both challenges and discuss the "lessons learned" from running such interdisciplinary projects.