The abundance and nature of high-redshift quiescent galaxies from JADES spectroscopy and the FLAMINGO simulations
Bibliographic record
Abstract
ABSTRACT We use NIRSpec/MSA (Micro Shutter Assembly) spectroscopy and NIRCam (Near-Infrared Camera) imaging to study a sample of 18 massive ($\log M_\star /\mathrm{M}_\odot \gt 10$ dex), central quiescent galaxies at $2\le z \le 5$ in the GOODS (Great Observatories Origins Deep Survey) fields, to investigate their number density, star formation histories, quenching time-scales, and incidence of active galactic nuclei (AGN). The data depth reaches $\log M_\star /\mathrm{M}_\odot \approx 9$ dex, yet the least-massive central quiescent galaxy found has $\log M_\star /\mathrm{M}_\odot \gt 10$ dex, suggesting that quenching is regulated by a physical quantity that scales with $M_\star$. With spectroscopy, we assess the completeness and purity of photometric samples, finding number densities 10 times higher than predicted by galaxy formation models, confirming earlier photometric studies. We compare our number densities to predictions from FLAMINGO (Full-Hydro Large-scale Structure Simulations with All-sky Mapping for the Interpretation of Next Generation Observations), the largest box full-hydro-simulation suite to date. We rule-out cosmic variance at the 3$\sigma$ level, providing spectroscopic confirmation that galaxy formation models do not match observations at $z>3$. Using FLAMINGO, we find that the vast majority of quiescent galaxies’ stars formed in situ, with these galaxies not having undergone multiple major dry mergers. This is in agreement with the compact observed size of these systems and suggests that major mergers are not a viable channel for quenching most massive galaxies. Several of our observed galaxies are old, with four displaying 4000 Å breaks with formation and quenching redshifts of $z\ge 8$ and $\ge 6$. Using tracers, we find that eight galaxies host AGN, including old systems, suggesting a high AGN duty cycle with a continuing trickle of gas to fuel accretion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".