2025ApJ...979....5L

Data-driven models for stellar spectra that depend on stellar labels suffer from label systematics which decrease model performance: the stellar labels gap. To close the stellar labels gap, we present a stellar label independent model for Gaia BP/RP spectra. We develop a novel implementation of a variational auto-encoder, which learns to generate an XP spectrum and accompanying scatter without relying on stellar labels. We demonstrate that our model achieves competitive XP spectra reconstructions in comparison to stellar label dependent models. We find that our model learns stellar properties directly from the data itself. We then apply our model to XP/APOGEE giant stars to study the [α/M] information in Gaia XP. We provide strong evidence that the XP spectra contain meaningful [α/M] information by demonstrating that our model learns the α-bimodality, without relying on stellar label correlations for stars with T_eff < 5000 K, while also being sensitive to the anomalous abundances of Gaia-Enceladus stars. We have publicly released our trained model, codebase and data. Importantly, our stellar label independent model can be implemented for any and all XP spectra because our model's performance scales with training object density, not training label density.