Galaxy quenching time-scales from a forensic reconstruction of their colour evolution
Bibliographic record
Abstract
ABSTRACT The time-scales on which galaxies move out of the blue cloud to the red sequence ($\tau ^{}_\mathrm{Q}$ ) provide insight into the mechanisms driving quenching. Here, we build upon previous work, where we showcased a method to reconstruct the colour evolution of observed low-redshift galaxies from the Galaxy And Mass Assembly (GAMA) survey based on spectral energy distribution (SED) fitting with ProSpect, together with a statistically driven definition for the blue and red populations. We also use the predicted colour evolution from the shark semi-analytic model, combined with SED fits of our simulated galaxy sample, to study the accuracy of the measured $\tau ^{}_\mathrm{Q}$ and gain physical insight into the colour evolution of galaxies. In this work, we measure $\tau ^{}_\mathrm{Q}$ in a consistent approach for both observations and simulations. After accounting for selection bias, we find evidence for an increase in $\tau ^{}_\mathrm{Q}$ in GAMA as a function of cosmic time (from $\tau ^{}_\mathrm{Q}$ ∼ 1 Gyr to $\tau ^{}_\mathrm{Q}$ ∼ 2 Gyr in the lapse of ∼4 Gyr), but not in shark ($\tau ^{}_\mathrm{Q}$ ≲ 1 Gyr). Our observations and simulations disagree on the effect of stellar mass, with GAMA showing massive galaxies transitioning faster, but is the opposite in shark. We find that environment only impacts galaxies below ∼1010 M⊙ in GAMA, with satellites having shorter $\tau ^{}_\mathrm{Q}$ than centrals by ∼0.4 Gyr, with shark only in qualitative agreement. Finally, we compare to previous literature, finding consistency with time-scales in the order of couple Gyr, but with several differences that we discuss.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 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".