Nora Shipp

Stellar streams, the tidal remnants of accreted globular clusters and dwarf galaxies, are uniquely powerful tools for studying the Milky Way. They provide strong constraints on the local distribution of dark matter on large and small scales, and offer insight into the formation and evolution of our Galaxy. These studies require a large, well-observed sample of stellar streams with full six-dimensional (6D) position and velocity measurements. Until recently, such a population was unattainable, but with the advent of large astronomical surveys, observations of stellar streams have improved significantly in recent years.

In this talk, I will present my work on assembling and modelling the first population of stellar streams in 6D. I will first present the discovery of a large sample of stellar streams in the Dark Energy Survey, which increased the known population of streams by ∼ 50%, as well as the discovery of a unique stellar stream associated with the Palomar 13 globular cluster. I will then describe the measurement of the velocities of this new sample of stellar streams via spectroscopic survey and with data from the Gaia satellite, and present results of modeling this population of stellar streams to constrain the distribution of matter in our Galaxy, including the mass of the Milky Way’s largest satellite, the Large Magellanic Cloud. Finally, I will conclude and discuss future efforts in the study of near-field cosmology with this population of 6D stellar streams.

Andrew Neil

Large-scale surveys of transiting exoplanets such as Kepler have revolutionized the study of exoplanet demographics. This dissertation focuses on the insights into planetary composition that can be gained by combining transit surveys with constraints on those planets' masses from radial velocity follow-up and transit timing variations. We first investigate second-order effects on the empirical planet mass-radius relation, finding the sample of Kepler and other transiting planets from small surveys with mass measurements to be consistent with no dependence on host star mass. We then show how the joint mass-radius-period distribution of planets can be constrained using a mixture model to include several compositional subpopulations. We create a suite of models and employ model selection techniques to show that the inclusion of at least three subpopulations (planets with gaseous envelopes, evaporated rocky cores, and intrinsically rocky planets) is supported by the data. We find similar support for models that include or exclude photoevaporation, as well as models that include or exclude water worlds, highlighting the degeneracies inherent to the planet population in the mass-radius-period plane. We use our models to calculate "Eta Earth", the occurrence rate at Earth's radius and period, finding our estimate to be highly model dependent and obtaining a significantly lower Eta Earth when photoevaporation is included in the model. Finally, we evolve a dense grid of planet evolution models that can enable future population studies to translate between the fundamental properties of planets (mass, envelope mass fraction, incident flux, age) and the observable plane (mass, radius, period). This thesis builds towards the robust characterization of the exoplanet composition distribution which will provide insights into competing theories of planet formation and the occurrence rate of habitable planets in the galaxy.

Gregory Gilbert

Building a complete and accurate census of exoplanets and exoplanetary systems is a keystone endeavor for understanding the planet formation process and for constraining the prevalence of habitable worlds in our Galaxy. However, in most cases, our knowledge is data-limited: observed demographic trends are equally well explained by several distinct population synthesis models, and planet formation simulations surpass our ability to observationally constrain theoretical predictions. Because data collection is expensive, forward progress often hinges on the development of new analysis techniques in order to wring the maximum information content from large archival datasets. By borrowing state-of-the-art methods from widely disparate fields such as complexity theory and biochemistry, I demonstrate that even the nearly decade-old Kepler dataset still holds many secrets waiting to be revealed. I present a new framework for classifying exoplanetary systems architectures, followed by a new method for analyzing photometric transit lightcurves which resolves a long-standing problem in exoplanet science and produces robust, unbiased estimates of planet parameters.