Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 85-89).

The technique of searching for extra-solar planetary transits is investigated. This technique, which relies on detecting the brief, shallow eclipses caused by planets passing across the line of sight to the primary star, requires high-precision time-series photometry of large numbers of stars in order to detect these statistically rare events. Observations of ~ 18000 stars in the field including the intermediate-age open cluster NGC 6819 are presented. This target field contrasts with the stellar environment surveyed by the radial velocity technique, which concentrates on the Solar neighbourhood. I present the data-reduction techniques used to obtain high-precision photometry in a semi-automated fashion for tens of thousands of stars at a time, together with an algorithm designed to search the resulting lightcurves for the transit signatures of hot Jupiter type planets. I describe simulations designed to test the detection efficiency of this algorithm and, for comparison, predict the number of transits expected from this data, assuming that hot Jupiter planets similar to HD 209458 are as common in the field of NGC 6819 as they are in the Solar neighbourhood. While no planetary transits have yet been identified, the detection of several very low amplitude eclipses by stellar companions demonstrates the effectiveness of the method. This study also indicates that stellar activity and particularly blending are significant causes of false detections. A useful additional consequence of studying this time-series photometry is the census it provides of some of the variable stars in the field. I report on the discovery of a variety of newly-discovered variables, including Algol-type detached eclipsing binaries which are likely to consist of M-dwarf stars. Further study of these stars is strongly recommended in order to help constrain models of stellar structure at the very low mass end. I conclude with a summary of this work in the context of other efforts being made in this field and recommend promising avenues of further study.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 156-159).

Thesis (Ph. D.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (p. 409-433). Also available via the World Web.

In this Thesis, I explore transiting exoplanets: what we can learn from modeling their light curves, and what we can learn from their arrangement in planetary systems. I begin in Chapter 1 by briefly reviewing the history of transit modeling, from the earliest theoretical models of eclipsing binary stars to the models in current widespread use to model exoplanet transits. In Chapter 2, I model the transits of a sample of Kepler exoplanets with strong prior eccentricity constraints in order to derive correspondingly strong constraints on the density of their host stars, and find that the density constraints I derive are as precise as density constraints from asteroseismology if the transits are observed at high signal-to-noise. In Chapter 3, I apply the same methodology in reverse: using prior knowledge of the stellar density based on Gaia parallax measurements, I model the transits of twelve singly-transiting planets observed by K2 and derive constraints on their periods. In Chapter 4, I consider the general problem of deducing the shape of a transiting object from its light curve alone, which I term ``shadow imaging;'' I explore the mathematical degeneracies of the problem and construct shadow images to explain Dips 5 and 8 of Boyajian's Star. I next turn to multi-planet systems: in Chapter 5, I investigate the underlying multiplicity distribution of planetary systems orbiting FGK dwarfs observed by Kepler. I find that we can explain the multiplicities of these systems with a single Zipfian multiplicity distribution, without invoking a dichotomous population. In Chapter 6, I consider the arrangement of planets in those systems, and use neural networks inspired by models used for part-of-speech tagging in computational linguistics to model the relationship between exoplanets and their surrounding "context," i.e. their host star and sibling planets. I find that our trained regression model is able to predict the period and radius of an exoplanet to a factor of two better than a naive model which only takes into account basic dynamical stability. I also find that our trained classification model identifies consistent classes of planets in the period-radius plane, and that it is rare for multi-planet systems to contain a neighboring pair of planets from non-contiguous classes. In Chapter 7, I summarize these results and briefly discuss avenues for future work, including the application of our methods to planets and planetary systems discovered by TESS.