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.
In this seminar, I will focus on two aspects of characterising extrasolar planets: the data analysis and the atmospheric inverse modelling. In the first part of my talk, I will discuss how we can use machine learning and deep autoregressive models to de-trend exoplanet time-series observations from instrumental and astrophysical noise. In the second part, I will discuss our recent work on developing Explainable AI approaches for exoplanet atmospheric modelling. By making these ‘black box’ models more interpretable, we begin to understand how different neural net architectures learn to model atmospheric spectra. This allows us to derive more robust prediction uncertainties as well as map information content as function of wavelength. As data and model complexities are bound to increase dramatically with the advent of JWST and ELT measurements, robust and interpretable deep learning models will become valuable tools in our data analysis repertoire.
