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Dreaming of Atmospheres

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If you have a question about this talk, please contact Ed Gillen.

The field of exoplanetary spectroscopy is as fast moving as it is new. Analysing currently available observations of exoplanetary atmospheres often invoke large and correlated parameter spaces that can be difficult to map or constrain. This is true for both: the data analysis of observations as well as the theoretical modelling of their atmospheres. Modelling both sets of correlations in data and modelling is key to understanding the nature of exoplanet atmospheres.

In this seminar I will discuss how these improvements in machine learning can be applied to exoplanetary spectroscopy to solve some of said correlations in the parameter space. In particular, I will discuss how concepts of information theory and statistical entropy can be used to solve some long standing problems in the data analysis and interpretation of exoplanetary atmospheric data. This move to a uniform modelling of exoplanet data will allow us to gain new access underlying correlations in the atmospheric physics of exoplanets.

This talk is part of the Exoplanet Meetings series.

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