Self-Supervised Representation Learning for JWST Galaxy Images and Low Resolution Spectra

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 $1<z\le3$ with $R^2 \sim 0.9$ and $\sigma_{\mathrm{NMAD}} = 0.24$~dex; cross-modal CLIP alignment further raises single-modality performance by $\Delta R^2 = +0.04$ (imaging) and $+0.09$ (spectroscopy), narrowing the gap to joint-modality inference. The code and documentation are available at (GitHub) https://github.com/2003cy/neurips2026_ssl_jwst .