The GOGREEN survey: dependence of galaxy properties on halo mass at <i>z</i> &gt; 1 and implications for environmental quenching
Bibliographic record
Abstract
ABSTRACT We use photometric redshifts and statistical background subtraction to measure stellar mass functions in galaxy group-mass (4.5−8 × 1013 M⊙) haloes at 1 < z < 1.5. Groups are selected from COSMOS and SXDF, based on X-ray imaging and sparse spectroscopy. Stellar mass (Mstellar) functions are computed for quiescent and star-forming galaxies separately, based on their rest-frame UVJ colours. From these we compute the quiescent fraction and quiescent fraction excess (QFE) relative to the field as a function of Mstellar. QFE increases with Mstellar, similar to more massive clusters at 1 < z < 1.5. This contrasts with the apparent separability of Mstellar and environmental factors on galaxy quiescent fractions at z ∼ 0. We then compare our results with higher mass clusters at 1 < z < 1.5 and lower redshifts. We find a strong QFE dependence on halo mass at fixed Mstellar; well fit by a logarithmic slope of d(QFE)/dlog (Mhalo) ∼ 0.24 ± 0.04 for all Mstellar and redshift bins. This dependence is in remarkably good qualitative agreement with the hydrodynamic simulation bahamas, but contradicts the observed dependence of QFE on Mstellar. We interpret the results using two toy models: one where a time delay until rapid (instantaneous) quenching begins upon accretion to the main progenitor (‘no pre-processing’) and one where it starts upon first becoming a satellite (‘pre-processing’). Delay times appear to be halo mass-dependent, with a significantly stronger dependence required without pre-processing. We conclude that our results support models in which environmental quenching begins in low-mass (<1014M⊙) haloes at z > 1.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".