Add to Calendar
A UV to IR Portrait of the Milky Way
Cat Fielder, University of Pittsburgh
Understanding where the Milky Way fits in amongst the broader galaxy population is critical for bridging the gap between detailed studies that are within our own galaxy (via SDSS-IV APOGEE and SDSS V Milky Way Mapper for example) and the observed trends in external galaxies (via MaNGA for example). Our previous work on using Milky Way analog galaxies in SDSS to predict the Milky Way’s optical colors used just two properties to define analogs (stellar mass and star formation rate), but there are other properties useful for selecting analogs (e.g., disk scale length or axis ratio). However, the number of analog galaxies rapidly approaches zero as more selection properties are included. To address this issue, I present a new method using Gaussian Process Regression that allows for consideration of up to six parameters simultaneously while still giving robust predictions of broad-band fluxes. I will present results using these improved methods on the color and luminosity of the Milky Way, and will demonstrate how the inferred properties of the Milky Way compare to transitional/green valley populations. I find that the Milky Way is still experiencing enough star formation to appear more blue than the green valley in both the ultraviolet and infrared.