The Evolution of the Star Formation Activity in Galaxies and Its Dependence on Environment
Bibliographic record
Abstract
We study how the proportion of star-forming galaxies evolves between z = 0.8 and 0 as a function of galaxy environment, using the O II line in emission as a signature of ongoing star formation. Our high- z data set comprises 16 clusters, 10 groups, and another 250 galaxies in poorer groups and the field at z = 0.4-0.8 from the ESO Distant Cluster Survey, plus another 9 massive clusters at similar redshifts. As a local comparison, we use galaxy systems selected from the Sloan Digital Sky Survey (SDSS) at 0.04 < z < 0.08. At high z most systems follow a broad anticorrelation between the fraction of star-forming galaxies and the system velocity dispersion. At face value, this suggests that at z = 0.4-0.8 the mass of the system largely determines the proportion of galaxies with ongoing star formation. At these redshifts the strength of star formation (as measured by the O II equivalent width) in star-forming galaxies is also found to vary systematically with environment. SDSS clusters have much lower fractions of star-forming galaxies than clusters at z = 0.4-0.8 and, in contrast with the distant clusters, show a plateau for velocity dispersions ≥550 km s -1 , where the fraction of galaxies with O II emission does not vary systematically with velocity dispersion. We quantify the evolution of the proportion of star-forming galaxies as a function of the system velocity dispersion and find that it is strongest in intermediate-mass systems (σ ~ 500-600 km s -1 at z = 0). To understand the origin of the observed trends, we use the Press-Schechter formalism and the Millennium Simulation and show that galaxy star formation histories may be closely related to the growth history of clusters and groups. If the scenario we propose is roughly correct, the link between galaxy properties and environment is extremely simple to predict purely from a knowledge of the growth of dark matter structures.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".