Institute for Advanced Study Astrophysics Seminar

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 applying these approaches to large datasets (including some fun interactive data visualizations). If time permits, I will also plan to discuss ongoing work using probabilistic machine learning to expand on these results with new low-resolution BPRP spectra from Gaia DR3, as well as associated efforts to ensure these approaches can successfully disentangle stellar age estimation from Galactic chemodynamical evolution.