Self-Supervised Representation Learning for JWST Galaxy Images and Low Resolution Spectra
Yang Cheng ⋅ Morgan Fouesneau ⋅ Ivelina G Momcheva ⋅ Tobias Buck ⋅ Shengxiu Sun
Abstract
A self-supervised multimodal model recovers galaxy stellar masses from sparse \emph{JWST} data that lack the multi-band coverage required by traditional SED fitting. We train on single-band F150W imaging paired with observed-frame $1$--$2 ~ \mu$m PRISM spectra and obtain a joint image-spectrum representation that recovers stellar mass at $1
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