Across the green valley with<i>HST</i>grisms: colour evolution, crossing time-scales, and the growth of the red sequence at<i>z</i> = 1.0–1.8
Bibliographic record
Abstract
ABSTRACT We measure the colour evolution and quenching time-scales of z = 1.0–1.8 galaxies across the green valley. We derive rest-frame NUVrK colours and select blue-cloud, green-valley, and red-sequence galaxies from the spectral energy distribution modelling of CANDELS GOODS-South and UDS multiband photometry. Separately, we constrain the star-formation history (SFH) parameters (ages, τ) of these galaxies by fitting their deep archival HST grism spectroscopy. We derive the galaxy colour–age relation and show that only rapidly evolving galaxies with characteristic delayed-τ SFH time-scales of <0.5 Gyr reach the red sequence at these redshifts, after a period of accelerated colour evolution across the green valley. These results indicate that the stellar mass build-up of these galaxies stays minimal after leaving the blue cloud and entering the green valley (i.e. it may represent $\lesssim 5{{\ \rm per\ cent}}$ of the galaxies’ final, quiescent masses). Visual inspection of age-sensitive features in the stacked spectra also supports the view that these galaxies follow a quenching sequence along the blue-cloud → green-valley → red-sequence track. For this rapidly evolving population, we measure a green-valley crossing time-scale of $0.99^{+0.42}_{-0.25}$ Gyr and a crossing rate at the bottom of the green valley of $0.82^{+0.27}_{-0.25}$ mag Gyr−1. Based on these time-scales, we estimate that the number density of massive (M⋆ > 1010M⊙) red-sequence galaxies doubles every Gyr at these redshifts, in remarkable agreement with the evolution of the quiescent galaxy stellar mass function. These results offer a new approach to measuring galaxy quenching over time and represent a pathfinder study for future JWST, Euclid, and Roman Space Telescope programs.
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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 | 0.000 | 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".