Hot subdwarf binaries with deepL from Gaia and SDSS

We applied this framework to a catalog of 61 585 hot subdwarf candidates. The resulting candidates were further examined through spectral energy distribution fitting, and atmospheric parameters were derived for high-quality systems. We identified 1369 hot subdwarf binary candidates from the parent sample.

Among them, 1057 objects were strongly supported as binaries by spectral energy distribution fitting. Atmospheric parameters were obtained for 579 high-quality binary systems. The interpretability analysis further indicates that the model decisions are consistent with known physical priors.

This work provides a substantially enlarged sample of hot subdwarf binary candidates and demonstrates the potential of multimodal deep learning for binary identification. The released catalog offers a useful basis for future studies on the formation and evolution of hot subdwarf binaries.