Statistical Challenges in Stellar Parameter Estimation from Theory and Data.
Date: Tuesday, 12 April 2022
Time: 16:00 (UTC) 3:00 (AEDT)
Speaker: Josh Speagle (Toronto University, Canada)
Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au
Abstract: Understanding how the Milky Way fits into the broader galaxy population requires studying the properties of other galaxies as well as our own. While it is possible to observe the structure of other galaxies directly, understanding the structure of our own Galaxy from within requires inferring the 3-D positions, velocities, and other properties of billions of stars. In this talk, I will discuss some of the statistical challenges in inferring stellar parameters from modern photometric surveys such as Gaia and SDSS, focusing in particular on issues with existing theoretical stellar models, the complex nature of parameter uncertainties, and scalability to large datasets. I will then describe some ongoing work trying to solve these problems using a combination of physics-inspired but data-driven calibrations along with a host of inference approaches including gradient-based optimization, grid-based searches, importance sampling, and nested sampling.
