TL;DR
This paper uses Gaussian Process Regression trained on SDSS galaxy data to estimate the Milky Way's full UV-to-IR spectral energy distribution as seen from an external perspective, revealing its star-forming nature.
Contribution
It introduces a novel application of GPR to predict the Milky Way's UV-IR SED, enabling direct comparison with external galaxy observations and improving understanding of our Galaxy's properties.
Findings
The Milky Way is in the star-forming region in UV and IR diagnostics.
The predicted SED aligns with local spiral galaxy characteristics.
The method allows for reconstructing the MW's star formation history comparable to external galaxy studies.
Abstract
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 ), optical (SDSS ) and IR (2MASS and WISE ) 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…
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