Mapping the Milky Way with Gaia Bp/Rp spectra

We train a neural network (NN) using stars in the Apache Point Observatory Galactic Evolution Experiment (APOGEE) to predict flux corrections as a function of wavelength for each target. Based on the comparison with APOGEE parameters, we conclude that our estimated parameters have systematic errors and uncertainties in Teff, logg, and [M/H] about -38+/-167K, 0.05+/-0.40dex, and -0.12+/-0.19dex, respectively, for stars in the range 4000<=Teff<=7000K.

The corrected Bp/Rp spectra show improved agreement with both models and Hubble Space Telescope (HST) CALSPEC data. Our correction increases the precision of the relative spectrophotometry of the Bp/Rp data from 3.2-3.7% to 1.2-2.4%. We also compare our results with other similar catalogs from the literature and validate them using star clusters.

Finally, we have built a catalog of atmospheric parameters for stars within 4000<=Teff<=7000K, comprising 68394431 sources, along with a subset of 124 188 stars with [M/H]<=-2.5. Our catalogs and flux correction code are publicly available. Our results confirm that the Gaia Bp/Rp flux calibrated spectra show systematic patterns as a function of wavelength that are tightly related to colors, magnitudes, and extinction.

Our optimization algorithm can give us accurate atmospheric parameters of stars with a clear and direct link to models of stellar atmospheres, and can be used to efficiently search for extremely metal-poor (EMP) stars.