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. However, their often ‘black box’ nature and unintuitive decision processes, are a key hurdle to their broader adoption. In this seminar, I will give an overview of deep learning approaches used in exoplanet characterisation and discuss our recent work on developing Explainable AI (XAI) approaches. XAI is a rapidly developing field in machine learning and aims to make ‘black box’ models interpretable. By understanding how different neural net architectures learn to interpret atmospheric spectra, we can 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.
This seminar will be 100% virtual (Zoom)
Organizer: Frédéric GALLIANO
