The VIMOS Public Extragalactic Redshift Survey (VIPERS). The decline of cosmic star formation: quenching, mass, and environment connections
Bibliographic record
Abstract
[Abridged] We use the final data of the VIMOS Public Extragalactic Redshift Survey (VIPERS) to investigate the effect of environment on the evolution of galaxies between $z=0.5$ and $z=0.9$. We characterise local environment in terms of the density contrast smoothed over a cylindrical kernel, the scale of which is defined by the distance to the $5^{th}$ nearest neighbour. We find that more massive galaxies tend to reside in higher-density environments over the full redshift range explored. Defining star-forming and passive galaxies through their (NUV$-r$) vs ($r-K$) colours, we then quantify the fraction of star-forming over passive galaxies, $f_{\rm ap}$, as a function of environment at fixed stellar mass. $f_{\rm ap}$ is higher in low-density regions for galaxies with masses ranging from $\log(\mathcal{M}/\mathcal{M}_\odot)=10.38$ (the lowest value explored) to at least $\log(\mathcal{M}/\mathcal{M}_\odot)\sim11.3$, although with decreasing significance going from lower to higher masses. This is the first time that environmental effects on high-mass galaxies are clearly detected at redshifts as high as $z\sim0.9$. We compared these results to VIPERS-like galaxy mock catalogues based on the galaxy formation model of De Lucia & Blaizot. The model correctly reproduces $f_{\rm ap}$ in low-density environments, but underpredicts it at high densities. The discrepancy is particularly strong for the lowest-mass bins. We find that this discrepancy is driven by an excess of low-mass passive satellite galaxies in the model. Looking at the accretion history of these model galaxies, i.e. the times when they become satellites, a better (yet not perfect) agreement with observations can be obtained in high density regions by assuming either that a not-negligible fraction of satellites is destroyed, or that their quenching time-scale is longer than $\sim 2$ Gyr.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".