GOGREEN: A critical assessment of environmental trends in cosmological hydrodynamical simulations at <i>z</i> ≈ 1
Bibliographic record
Abstract
ABSTRACT Recent observations have shown that the environmental quenching of galaxies at z ∼ 1 is qualitatively different to that in the local Universe. However, the physical origin of these differences has not yet been elucidated. In addition, while low-redshift comparisons between observed environmental trends and the predictions of cosmological hydrodynamical simulations are now routine, there have been relatively few comparisons at higher redshifts to date. Here we confront three state-of-the-art suites of simulations (BAHAMAS+MACSIS, EAGLE+Hydrangea, IllustrisTNG) with state-of-the-art observations of the field and cluster environments from the COSMOS/UltraVISTA and GOGREEN surveys, respectively, at z ∼ 1 to assess the realism of the simulations and gain insight into the evolution of environmental quenching. We show that while the simulations generally reproduce the stellar content and the stellar mass functions of quiescent and star-forming galaxies in the field, all the simulations struggle to capture the observed quenching of satellites in the cluster environment, in that they are overly efficient at quenching low-mass satellites. Furthermore, two of the suites do not sufficiently quench the highest mass galaxies in clusters, perhaps a result of insufficient feedback from AGN. The origin of the discrepancy at low stellar masses ($M_* \lesssim 10^{10}$ M⊙), which is present in all the simulations in spite of large differences in resolution, feedback implementations, and hydrodynamical solvers, is unclear. The next generation of simulations, which will push to significantly higher resolution and also include explicit modelling of the cold interstellar medium, may help us to shed light on the low-mass tension.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".