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The recent release of 220+ million BP/RP spectra in $\textit{Gaia}$ DR3presents an opportunity to apply deep learning models to an unprecedentednumber of stellar spectra, at extremely low-resolution. The BP/RP dataset is somassive that no previous spectroscopic survey can provide enough stellar labelsto cover the BP/RP parameter space. We present an unsupervised, deep,generative model for BP/RP spectra: a $\textit{scatter}$ variationalauto-encoder. We design a non-traditional variational auto-encoder which iscapable of modeling both $(i)$ BP/RP coefficients and $(ii)$ intrinsic scatter.Our model learns a latent space from which to generate BP/RP spectra (scatter)directly from the data itself without requiring any stellar labels. Wedemonstrate that our model accurately reproduces BP/RP spectra in regions ofparameter space where supervised learning fails or cannot be implemented.

Closing the stellar labels gap: An unsupervised, generative model for $\textit{Gaia}$ BP/RP spectra