Hollis, MDJ; (2014) Characterisation of Extrasolar Planets: Applications to radial velocity cataloguing and atmospheric radiative transfer. Doctoral thesis , UCL (University College London).

Abstract

This thesis concerns the cataloguing and characterisation of extrasolar planets, an important topic given its potential to inform theories of planet formation and evolution, and its relevance for future studies defining and assessing the habitability of other worlds. The first aspect of the study is the calculation of orbits, using radial velocity measurements coupled with Bayesian and Markov chain Monte Carlo methods, to produce a catalogue of orbital elements for a sizeable sample of planets. This constitutes a self-consistent, uniformly-derived catalogue, useful for statistical planetary population and formation studies, to be contrasted with other databases of planetary parameters, which are in general compilations of measurements from different sources and using various techniques. The orbital elements determine important star-on-planet forcings (for example ultra-violet irradiation, which has significant impacts on planetary (photo)chemistry and dynamics), and this study also looks at characterising planets explicitly in terms of their atmospheres. A 1D radiative transfer model for planetary transmission spectroscopy has been produced, and made freely-available for use by the community. This method is particularly useful since it allows the retrieval of first-order abundances of trace atmospheric molecules, which in turn can be used to estimate parameters such as the C/O ratio, potentially providing further constraints on planetary formation processes. The code in question has been validated by comparison to models in the literature, and applied to several real planetary atmospheres. It has also been extended by incorporating a method to estimate the opacity due to scattering particles in clouds and haze layers. If present in an atmosphere such phenomena can lead to the persistence of various parameter degeneracies, and limit the extent to which inferences can be drawn from spectra (leading to potentially order-of-magnitude errors in estimates of molecular abundances). Future extensions to this work could include the development of an automated inversion framework, utilising joint Bayesian/Markov chain Monte Carlo techniques to explore the parameter space of all relevant atmospheric quantities in order to retrieve a complete solution that is consistent with observations.

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