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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">〈</mml:mo> <mml:msub> <mml:mrow> <mml:mi>τ</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>quench</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">〉</mml:mo> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>1.0</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>0.9</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.8</mml:mn> </mml:mrow> </mml:msubsup> <mml:mspace width="0.50em"/> <mml:mi>Gyr</mml:mi> </mml:math> and of quenching epochs of z quench ≈ 0.8–5.0 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">〈</mml:mo> <mml:msub> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>quench</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">〉</mml:mo> <mml:mo>=</mml:mo> <mml:msubsup> <mml:mrow> <mml:mn>1.3</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>0.4</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>0.7</mml:mn> </mml:mrow> </mml:msubsup> </mml:math> ). 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".