2024ApJS..272....2L

We present AspGap, a new approach to inferring stellar labels from the low-resolution Gaia XP spectra, including precise [α/M] estimates-the first time these are obtained by such an approach. AspGap is a neural-network-based regression model trained on APOGEE spectra. In the training step, AspGap learns to use not only XP spectra to predict stellar labels but also the high-resolution APOGEE spectra that lead to the same stellar labels. The inclusion of this last model component-dubbed the hallucinator-creates a more physically motivated mapping and significantly improves the prediction of stellar labels in the validation, particularly that of [α/M]. For giant stars, we find cross-validated rms accuracies for T_eff, log g, [M/H], and [α/M] of ∼1%, 0.12 dex, 0.07 dex, and 0.03 dex, respectively. We also validate our labels through comparison with external data sets and through a range of astrophysical tests that demonstrate that we are indeed determining [α/M] from the XP spectra, rather than just inferring it indirectly from correlations with other labels. We publicly release the AspGap codebase, along with our stellar parameter catalog for all giants observed by Gaia XP. AspGap enables the discovery of new insights into the formation and chemodynamics of our Galaxy by providing precise [α/M] estimates for 37 million giant stars, including 14 million with radial velocities from Gaia.