From Panchromatic Images to Stellar Ages and Beyond

Abstract: Large-area surveys such as Gaia have given us an unprecedented amount of data on stars in the Milky Way. One of the largest ongoing challenges in understanding the present-day structure and formation history of our Galaxy (i.e. Galactic archaeology) involves converting these data into estimates of stellar properties such as distances, metallicities, and ages. In this talk, I will outline some of the fundamental statistical and computational challenges involved in this process, the approaches my collaborators and I have taken to solve them, and the preliminary successes from applying these approaches to large datasets (including some fun interactive data visualizations). I will also plan to discuss ongoing work using probabilistic machine learning to expand on these results in a variety of domains, as time permits.