3) A UV-IR portrait of the Milky Way

Abstract: Understanding where the Milky Way fits in amongst the broader galaxy population is critical for bridging the gap between the local high detail studies that we can perform within our own galaxy (e.g., APOGEE and MWM) and the trends and relations that we observe in the external population (e.g., MaNGA). Our previous work on Milky Way analog galaxies in SDSS used just two properties (stellar mass and star formation rate), but there are other properties useful for selecting analogs (e.g., disk scale length, axis ratio). However, the number of analog galaxies rapidly approaches zero as more selection properties are included. To address this issue, we present a new method using Gaussian Process Regression that allows for consideration of up to six parameters simultaneously while still giving robust predictions. I will present results using these improved methods on the color and luminosity of the Milky Way, and how the inferred properties of the Milky Way compare to transitional/green valley populations.

Bio: I am a graduate student in my final year at the University of Pittsburgh. My work lays at the intersection of interpreting theoretical frameworks originating from dark matter simulations and improved observational constraints. I am interested in understanding how we can leverage the uniqueness of the Milky Way in order to fill in the gaps of our picture of it, and how we should extrapolate from the Milky Way in order to inform our simulations. I am actively involved with the SDSS-IV Milky Way as a galaxy group, and in mentoring younger students through the Women and Minorities in Physics group at the University of Pittsburgh.