Efficient satellite quenching at z∼1 from the GEEC2 spectroscopic survey of galaxy groups
Bibliographic record
Abstract
We present deep Gemini Multi-Object Spectrograph-South spectroscopy for 11 galaxy groups at 0.8 < z < 1.0, for galaxies with rAB < 24.75. Our sample is highly complete (>66 per cent) for eight of the 11 groups. Using an optical–near-infrared colour–colour diagram, the galaxies in the sample were separated with a dust insensitive method into three categories: passive (red), star-forming (blue) and intermediate (green). The strongest environmental dependence is observed in the fraction of passive galaxies, which make up only ∼20 per cent of the field in the mass range 1010.3 < Mstar/M⊙ < 1011.0, but are the dominant component of groups. If we assume that the properties of the field are similar to those of the ‘pre-accreted’ population, the environment quenching efficiency (ϵρ) is defined as the fraction of field galaxies required to be quenched in order to match the observed red fraction inside groups. The efficiency obtained is ∼0.4, similar to its value in intermediate-density environments locally. While green (intermediate) galaxies represent ∼20 per cent of the star-forming population in both the group and field, at all stellar masses, the average specific star formation rate of the group population is lower by a factor of ∼3. The green population does not show strong Hδ absorption that is characteristic of starburst galaxies. Finally, the high fraction of passive galaxies in groups, when combined with satellite accretion models, require that most accreted galaxies have been affected by their environment. Thus, any delay between accretion and the onset of truncation of star formation (τ) must be ≲ 2 Gyr, shorter than the 3–7 Gyr required to fit data at z = 0. The relatively small fraction of intermediate galaxies require that the actual quenching process occurs quickly, with an exponential decay time-scale of τq ≲ 1 Gyr.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".