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In Chapter 2 of this thesis I present my database of molecular absorption cross sections. These were developed using public molecular transition line-lists (from the ExoMol group). I use them to find limitations in the modelling of exoplanet atmospheres due to pressure broadening. Pressure broadening, where collisions between molecules in atmospheres cause a Lorentzian broadening of molecular transitional lines, is little understood in the field. In this chapter I consider its effects on real exoplanet atmosphere observations, both with current and future instruments. I show that pressure broadening may affect future observations of exoplanets in the JWST era. Pressure broadening primarily affects cooler, small exoplanets such as Earth analogues. In Chapter 3 I present the pipeline I have developed to reduce HST WFC3 spectra of exoplanet hosts during transits to create transmission spectra. This code corrects several instrumental systematics, from varying dark signal in the detector to subpixel shifts in the target position over time. By creating a pipeline to process all targets, regardless of observing strategy, systematics are dealt with uniformly and different planets’ spectra can be meaningfully compared. I show that the height of the water feature in 30 unique exoplanets’ transmission spectra is strongly correlated with the most simplistic absorption model. I use this to predict a list of the best future targets for observations with HST WFC3 to find water. In Chapter 4 I discuss my work with the stellar spectra from WFC3, which utilise the sub-pixel shifts in target position to oversample the spectra and increase the resolution. I have compared these exoplanet host stellar spectra with stellar models to investigate how well stellar atmosphere models describe the near IR. I find a small discrepancy in temperature when WFC3 alone is used to assess the stellar temperature, particularly with cooler stars. I attribute this firstly to an error in the WFC3 sensitivity curve and secondly to an inaccuracy in models of cool, small stars due to molecular absorption. In Chapter 5 I present my work on K2 light curve data using machine learning to find young stellar objects that display unusual, transit-like behaviour. These objects are known as dipper stars due to their distinctive occultations with depths of 10-50% in flux and very fast orbital periods of a few hours to a few days. Such large occultations are difficult to explain and are currently attributed to material at the inner edge of the protoplanetary disk. This behaviour is often variable and aperiodic, suggesting that the occulting material is changing in morphology on the time scale of a single orbit. Using python’s scikit-learn I have developed a code that utilises a Random Forest algorithm to classify stars in K2 Campaign Field 2 and distinguish these objects from other types of variables, such as eclipsing binaries and pulsating stars. This method has proved very successful and has allowed me to nearly quadruple the number of known dipper candidates in the Upper Scorpius and Rho Ophiuchus regions.

This thesis was carried out in two parts. The stellar spectral data was used from the Gaia-ESO survey. The data used was fromthe public archive as well as data received from Dr. Recio-Blanco at Observatoire Cote D'Azure. 1) I performed anomaly detection using unsupervised random forests, by applying the concept of weirdness scores to identify outliers. 2) Using spectral data along with physical parameters of objects in the galactic bulge of the Gaia-ESO survey, I built a variational autoencoder neural network to reconstruct stellar spectra and explore latent features learning physical parameters by themselves.

The combined observations of a planet’s transits and the radial velocity variations of its host star allow the determination of the planet’s orbital parameters, and most inter- estingly of its radius and mass, and hence its mean density. Observed densities provide important constraints to planet structure and evolution models. The uncertainties on the parameters of large exoplanets mainly arise from those on stellar masses and radii. For small exoplanets, the treatment of stellar variability limits the accuracy on the de- rived parameters. The goal of this PhD thesis was to reduce these sources of uncertainty by developing new techniques for stellar variability filtering and for the determination of stellar temperatures, and by robustly fitting the transits taking into account external constraints on the planet’s host star. To this end, I developed the Iterative Reconstruction Filter (IRF), a new post-detection stellar variability filter. By exploiting the prior knowledge of the planet’s orbital period, it simultaneously estimates the transit signal and the stellar variability signal, using a com- bination of moving average and median filters. The IRF was tested on simulated CoRoT light curves, where it significantly improved the estimate of the transit signal, particu- lary in the case of light curves with strong stellar variability. It was then applied to the light curves of the first seven planets discovered by CoRoT, a space mission designed to search for planetary transits, to obtain refined estimates of their parameters. As the IRF preserves all signal at the planet’s orbital period, t can also be used to search for secondary eclipses and orbital phase variations for the most promising cases. This en- abled the detection of the secondary eclipses of CoRoT-1b and CoRoT-2b in the white (300–1000 nm) CoRoT bandpass, as well as a marginal detection of CoRoT-1b’s orbital phase variations. The wide optical bandpass of CoRoT limits the distinction between thermal emission and reflected light contributions to the secondary eclipse. I developed a method to derive precise stellar relative temperatures using equiv- alent width ratios and applied it to the host stars of the first eight CoRoT planets. For stars with temperature within the calibrated range, the derived temperatures are con- sistent with the literature, but have smaller formal uncertainties. I then used a Markov Chain Monte Carlo technique to explore the correlations between planet parameters derived from transits, and the impact of external constraints (e.g. the spectroscopically derived stellar temperature, which is linked to the stellar density). Globally, this PhD thesis highlights, and in part addresses, the complexity of perform- ing detailed characterisation of transit light curves. Many low amplitude effects must be taken into account: residual stellar activity and systematics, stellar limb darkening, and the interplay of all available constraints on transit fitting. Several promising areas for further improvements and applications were identified. Current and future high precision photometry missions will discover increasing numbers of small planets around relatively active stars, and the IRF is expected to be useful in characterising them.

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