Josh Speagle is an Assistant Professor of Astrostatistics in the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. His work focuses on using a collection of astronomy, statistics, and data science to try and understand how galaxies like the Milky Way form, behave, and evolve.
Title: 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 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.
