Fast, Slow, Early, Late: Quenching Massive Galaxies at z ∼ 0.8
Bibliographic record
Abstract
Abstract We investigate the stellar populations for a sample of 161 massive, mainly quiescent galaxies at 〈 z obs 〉 = 0.8 with deep Keck/DEIMOS rest-frame optical spectroscopy (HALO7D survey). With the fully Bayesian framework Prospector , we simultaneously fit the spectroscopic and photometric data with an advanced physical model (including nonparametric star formation histories, emission lines, variable dust attenuation law, and dust and active galactic nucleus emission), together with an uncertainty and outlier model. We show that both spectroscopy and photometry are needed to break the dust–age–metallicity degeneracy. We find a large diversity of star formation histories: although the most massive ( M ⋆ > 2 × 10 11 M ⊙ ) galaxies formed the earliest (formation redshift of z f ≈ 5–10 with a short star formation timescale of τ SF ≲ 1 Gyr), lower-mass galaxies have a wide range of formation redshifts, leading to only a weak trend of z f with M ⋆ . Interestingly, several low-mass galaxies have formation redshifts of z f ≈ 5–8. Star-forming galaxies evolve about the star-forming main sequence, crossing the ridgeline several times in their past. Quiescent galaxies show a wide range and continuous distribution of quenching timescales ( τ quench ≈ 0–5 Gyr) with a median of 〈 τ quench 〉 = 1.0 − 0.9 + 0.8 Gyr and of quenching epochs of z quench ≈ 0.8–5.0 ( 〈 z quench 〉 = 1.3 − 0.4 + 0.7 ). This large diversity of quenching timescales and epochs points toward a combination of internal and external quenching mechanisms. In our sample, rejuvenation and “late bloomers” are uncommon. In summary, our analysis supports the “grow-and-quench” framework and is consistent with a wide and continuously populated diversity of quenching timescales.
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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 | 0.000 |
| 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) | 0.001 | 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".