Workshop: 2nd ICML Workshop on Machine Learning for Astrophysics

Closing the stellar labels gap: An unsupervised, generative model for Gaia BP/RP spectra

Alex Laroche · Joshua Speagle


The recent release of 220+ million BP/RP spectra in Gaia DR3 presents an opportunity to apply deep learning models to an unprecedented number of stellar spectra, at extremely low-resolution. The BP/RP dataset is so massive that no previous spectroscopic survey can provide enough stellar labels to cover the BP/RP parameter space. We present an unsupervised, deep, generative model for BP/RP spectra: a scatter variational auto-encoder. We design a non-traditional variational auto-encoder which is capable 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. We demonstrate that our model accurately reproduces BP/RP spectra in regions of parameter space where supervised learning fails or cannot be implemented.

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