The GOGREEN Survey: A deep stellar mass function of cluster galaxies at 1.0 < <i>z</i> < 1.4 and the complex nature of satellite quenching
Bibliographic record
Abstract
We study the stellar mass functions (SMFs) of star-forming and quiescent galaxies in 11 galaxy clusters at 1.0 < z < 1.4 drawn from the Gemini Observations of Galaxies in Rich Early ENvironments (GOGREEN) survey. Based on more than 500 h of Gemini/GMOS spectroscopy and deep multi-band photometry taken with a range of observatories, we probe the SMFs down to a stellar mass limit of 109.7 M⊙ (109.5 M⊙ for star-forming galaxies). At this early epoch, the fraction of quiescent galaxies is already highly elevated in the clusters compared to the field at the same redshift. The quenched fraction excess (QFE) represents the fraction of galaxies that would be star-forming in the field but are quenched due to their environment. The QFE is strongly mass dependent, and increases from ∼30% at M⋆ = 109.7 M⊙ to ∼80% at M⋆ = 1011.0 M⊙. Nonetheless, the shapes of the SMFs of the two individual galaxy types, star-forming and quiescent galaxies, are identical between cluster and field to high statistical precision. Nevertheless, along with the different quiescent fractions, the total galaxy SMF is also environmentally dependent, with a relative deficit of low-mass galaxies in the clusters. These results are in stark contrast with findings in the local Universe, and therefore require a substantially different quenching mode to operate at early times. We discuss these results in light of several popular quenching models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".