Mapping the Milky Way in the Age of Gaia

Abstract

A central problem in astronomy is converting the 2-D positions of sources observed on the sky to their 3-D positions in space. This has become especially important in the last decades as large-scale surveys such as Gaia are providing astrometric and photometric measurements of billions of sources. These datasets open up an entirely new regime for understanding galaxies such as our own Milky Way, provided we can develop the appropriate statistical algorithms to analyze them and the computational methods necessary to apply them at unprecedented scales. In pursuit of these goals, I present new computational and statistical algorithms both for general use as well as specifically to model the 3-D distribution of stars and dust using these large datasets. Using empirical stellar models, I derive new constraints on the 3-D distribution of nearby star-forming regions. Using state-of-the-art theoretical stellar models, I then derive the 3-D distribution of 170M stars at high Galactic latitudes along with their associated stellar properties. I also discuss methods used to characterize systematics in both the photometric data and the underlying stellar models. These results are accompanied by publicly available datasets, interactive figures, and open-source code that can be used by others to explore my results as well as further their own research interests.