Discovery of red galaxy candidates at z ~ 12: Early dust growth or significant nebular emission with high-temperature stars?
Bibliographic record
Abstract
We report the discovery of two z ~ 12 galaxy candidates with unusually red UV slopes (betaUV ~> -1.5), and probe the origin of such colors at cosmic dawn. From Prospector fits to the UNCOVER/MegaScience dataset -- deep JWST/NIRCam imaging of Abell 2744 in 20 broad- and medium-bands -- we identify several new z > 10 galaxies. Medium-band data improve redshift estimates, revealing two lensed (mu ~ 3.3) z ~ 12 galaxies in a close pair with beta_UV ~> -1.5 at an UV absolute magnitude of M_UV ~ -19 mag, lying away from typical scatter on previously known MUV-betaUV relations. SED fitting with Prospector, Bagpipes, and EAZY support their high-z nature, with probability of low-z interlopers of p(z < 7) < 10%. The potential low-z interlopers are z ~ 3 quiescent galaxies (QGs), but unexpected to be detected at the given field of view unless z ~ 3 QG stellar mass function has a strong turn up at log Mstar/Msun ~ 9. Unlike typical blue high-redshift candidates (beta_UV ~< -2.0), these red slopes require either dust or nebular continuum reddening. The dust scenario implies Av ~ 0.8 mag, which is larger than theoretical predictions, but is consistent with a dust-to-stellar mass ratio (log M_dust/M_star ~ -3). The nebular scenario demands dense gas (log nH /cm^3 ~ 4.0) around hot stars (log Teff [K] ~ 4.9). Spectroscopic follow-up is essential to determine their true nature and reveal missing galaxies at the cosmic dawn.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".