The VIMOS Public Extragalactic Redshift Survey (VIPERS)
Bibliographic record
Abstract
We use the final data of the VIMOS Public Extragalactic Redshift Survey (VIPERS) to investigate the effect of the 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 fifth nearest neighbour. This is performed by using a volume-limited sub-sample of galaxies complete up to z = 0.9, but allows us to attach a value of local density to all galaxies in the full VIPERS magnitude-limited sample to i < 22.5. We use this information to estimate how the distribution of galaxy stellar masses depends on environment. 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, fap, as a function of environment at fixed stellar mass. fap is higher in low-density regions for galaxies with masses ranging from log (ℳ/ℳ⊙) = 10.38 (the lowest value explored) to at least log (ℳ/ℳ⊙) ~ 11.3, 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 ~ 0.9. We compared these results to VIPERS-like galaxy mock catalogues based on a widely used galaxy formation model. The model correctly reproduces fap 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. In high-density regions, we obtain a better (although not perfect) agreement of the model fap with observations by studying the accretion history of these model galaxies (that is, the times when they become satellites), by assuming either that a non-negligible fraction of satellites is destroyed, or that their quenching timescale is longer than ~ 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".