Spectroscopy-informed Deep Learning Searches for Photometric Little Red Dots in JWST Surveys
A two-stage, spectroscopy-informed NIRCam classifier finds LRD candidates beyond standard color selections. Held-out recall/precision are 0.92/0.79, while broad Hα in 10 of 13 grating-observed candidates supports—without confirming—the LRD interpretation of many new selections.
What changes Rather than applying colors directly, this work transfers spectroscopic information into imaging representations. It reports 35 spectroscopic-sample candidates, 357 from Dawn JWST Archive imaging, and 23 from two further surveys. Candidates span broader observed SED properties than existing samples, which the authors interpret as possible evidence that color selection misses diversity. Recovery of one reported candidate in each of SAPPHIRES and BEACON supports cross-survey applicability, not a measured recovery fraction.
Figure 1 · Open paperFigure notes (3)
- Figure 1. Tests the methodological premise: PRISM spectral and NIRCam image embeddings are contrastively aligned before an image-only head is trained, so spectroscopy informs training rather than inference.
- Figure 5. Tests spectroscopic support for model-selected candidates by fitting Hα with minimum-BIC continuum-plus one- or two-Gaussian models; displayed 1σ spectral uncertainties and broad-component widths show the basis for the broad-line classification.
- Figure 7. Tests whether selected candidates occupy the observed F444W-magnitude–redshift region of known spectroscopic LRDs; it mixes spectroscopic redshifts where available with photometric redshifts otherwise.









