Cosmic stillness: high quiescent galaxy fractions across upper mass scales in the early Universe to <i>z</i> = 7 with <i>JWST</i>
Bibliographic record
Abstract
ABSTRACT We present a detailed investigation into the abundance and morphology of high-redshift quenched galaxies at $3 < z < 7$ using James Webb Space Telescope data in the NEP, CEERS, and JADES fields. Within these fields, we identify 90 candidate passive galaxies using specific star formation rates modelled with the BAGPIPES spectral energy distribution fitting code, which is more effective at identifying recently quenched systems than the classical UVJ method, which specializes in quenched objects $>$1 Gyr. With this sample of galaxies, we find number densities broadly consistent with other works and a rapidly evolving passive fraction of high-mass galaxies ($\log _{10}{(M_{\star }/{\rm M}_{\odot })} >$ 9.5) in the range $3 < z < 5$. We find that the fraction of galaxies with low star formation rates and mass 9.5 $ < \log _{10}{(M_{\star }/{\rm M}_{\odot })} <$ 10.5 decreases from $\sim$25 per cent at $3 < z < 4$ to $\sim$2 per cent at $5 < z < 7$. Our passive sample of galaxies is shown to exhibit more compact light profiles compared to star-forming counterparts and some exhibit traces of active galactic nucleus activity through detections in either the X-ray or radio. At the highest redshifts ($z > 6.5$) passive selections start to include examples of ‘little red dots’, which complicates any conclusions until their nature is better understood.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".