To High Redshift and Low Mass: Exploring the Emergence of Quenched Galaxies and Their Environments at 3 < z < 6 in the Ultra-deep JADES MIRI F770W Parallel
Bibliographic record
Abstract
Abstract We present the robust selection of high-redshift quiescent galaxies (QG) and poststarburst (PSB) galaxies using ultra-deep NIRCam and MIRI imaging from the JWST Advanced Deep Extragalactic Survey (JADES). At 3 < z < 6, MIRI 7.7 μ m imaging provides rest-frame J band, which is commonly used to break the degeneracy between old stellar populations and dust attenuation at lower redshifts. We identify 23 passively evolving galaxies in UVJ color space in a mass-limited (log M ⋆ / M ⊙ ≥ 8.5) sample over 8.8 arcmin 2 . An evaluation of the contribution of the 7.7 μ m shows that JADES-like NIRCam coverage (9+ photometric bands) can compensate for lacking the J band at these redshifts; however, more limited three-band selections perform better with MIRI. Our sample is characterized by rapid quenching timescales (∼100–600 Myr) with formation redshifts z f ≲ 9 and includes a potential record-holding massive QG at z phot = 5.33 − 0.17 + 0.16 and two QGs with evidence for significant residual dust content ( A V ∼ 1–2). In addition, we present a large sample of 12 log M ⋆ / M ⊙ = 8.5–9.5 PSBs, demonstrating that UVJ selection can be extended to low mass. An analysis of the environment of our sample reveals that the group known as the Cosmic Rose contains a massive QG and a dust-obscured star-forming galaxy (a so-called Jekyll and Hyde pair) plus three additional QGs within ∼20 kpc. Moreover, the Cosmic Rose is part of a larger overdensity at z ∼ 3.7, which contains 7/12 of our low-mass PSBs. Another four low-mass PSBs are members of an overdensity at z ∼ 3.4; this result strongly indicates low-mass PSBs are preferentially associated with overdense environments at z > 3.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".