Speaker
Xabier Pérez Couto
Description
We present a self-supervised contrastive learning approach for the classification of white dwarf atmospheric types using Gaia DR3 data. Our framework combines XP spectral coefficients with photometric and astrometric features through a multi-branch neural encoder, trained without spectroscopic labels using physically motivated augmentations. The resulting embeddings show substantially improved class separation compared to raw spectral coefficients and classical unsupervised methods, and enable the recovery of rare spectral subtypes that are undetectable with linear classifiers. We discuss the potential of this approach for automated white dwarf classification in the era of Gaia DR4 and future large-scale spectroscopic surveys.