Constraining the Milky Way's Ultraviolet to Infrared SED with Gaussian Process Regression
Improving our knowledge of global Milky Way (MW) properties is critical for connecting the detailed measurements only possible from within our Galaxy to our understanding of the broader galaxy population. We train Gaussian Process Regression (GPR) models on SDSS galaxies to map from galaxy properties (stellar mass, apparent axis ratio, star formation rate, bulge-to-total ratio, disk scale length, and bar vote fraction) to UV (GALEX $FUV/NUV$), optical (SDSS $ugriz$) and IR (2MASS $JHKs$ and WISE $W1/W2/W3/W4$) fluxes and uncertainties. With these models we estimate the photometric properties of the MW, resulting in a full UV-to-IR spectral energy distribution (SED) as it would be measured externally, viewed face-on. We confirm that the Milky Way lies in the green valley in optical diagnostic diagrams, but show for the first time that the MW is in the star-forming region in standard UV and IR diagnostics -- characteristic of the population of red spiral galaxies. Although our GPR method predicts one band at a time, the resulting MW UV--IR SED is consistent with SEDs of local spirals with characteristics broadly similar to the MW, suggesting that these independent predictions can be combined reliably. Our UV--IR SED will be invaluable for reconstructing the MW's star formation history using the same tools employed for external galaxies, allowing comparisons of results from \textit{in situ} measurements to those from the methods used for extra-galactic objects.