The evolution of star formation activity in galaxy groups
Bibliographic record
Abstract
We study the evolution of the total star formation (SF) activity, total stellar mass (ΣM*) and halo occupation distribution (HOD) in massive haloes by using one of the largest X-ray selected sample of galaxy groups with secure spectroscopic identification in the major blank field surveys (ECDFS, CDFN, COSMOS, AEGIS). We provide an accurate measurement of star formation rate (SFR) for the bulk of the star-forming galaxies using very deep mid-infrared Spitzer MIPS and far-infrared Herschel PACS observations. For undetected IR sources, we provide a well-calibrated SFR from spectral energy distribution (SED) fitting. We observe a clear evolution in the level of SF activity in galaxy groups. The total SF activity in the high-redshift groups (0.5 < z < 1.1) is higher with respect to the low-redshift (0.15 < z < 0.5) sample at any mass by 0.8 ± 0.12 dex. A milder difference (0.35 ± 0.1 dex) is observed between the low-redshift bin and the groups at z ∼ 0. We show that the level of SF activity is declining more rapidly in the more massive haloes than in the more common lower mass haloes. We do not observe any evolution in the HOD and total stellar mass–halo mass relations in groups. The picture emerging from our findings suggests that the galaxy population in the most massive systems is evolving faster than galaxies in lower mass haloes, consistently with a ‘halo downsizing’ scenario.
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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 | 0.002 | 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) | 0.001 | 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".