Learning JWST. I. A Foundation Model for New Population Discoveries and Morphology-Aware Photometric Redshift Measurements in the JADES Survey

Abstract

We present FM-JADES-v1, a self-supervised foundation model for James Webb Space Telescope ({\em JWST}) deep-field science, trained with 482,444 objects from the {\em JWST} Advanced Deep Extragalactic Survey (JADES) Data Release 5 using multi-band imaging and the photometric catalog. The shared embedding space is trained without class labels. We demonstrate that FM-JADES-v1 can serve as a powerful tool for object discovery and improving property measurements using two experiments, blind active discovery and few-band photometric redshift. For blind object discovery, FM-JADES-v1 identifies rare object populations such as high-redshift galaxies and Little Red Dots (LRDs) without any prior population labels or population-specific selection criteria. These rare populations emerge as isolated islands in the embedding space, which can be identified without prior astrophysical knowledge. For few-band photometric redshift, FM-JADES-v1's learned embeddings achieve $\sigma_{\rm NMAD}=0.157$ in a strictly controlled three-band (F115W/F200W/F356W) photo-$z$ benchmark, compared to $\sigma_{\rm NMAD}= 0.44$ for template fitting. These results demonstrate the potential of self-supervised multi-modal representations as scalable discovery spaces for large astronomical surveys. Applied to ongoing and future wide-field surveys from JWST, Roman, Euclid, and Rubin/LSST, this framework could enable systematic searches for rare populations, as well as enabling multiple downstream tasks such as improving astrophysical property measurements.