The VIMOS Public Extragalactic Redshift Survey (VIPERS)
Bibliographic record
Abstract
We use the unparalleled statistics of the VIPERS survey to investigate the relation between the surface mean stellar mass density Σ = ℳ/(2 πRe2) of massive passive galaxies (MPGs, ℳ ≥ 1011 M⊙) and their local environment in the redshift range 0.5 ≤ z ≤ 0.8. Passive galaxies were selected on the basis of their NUVrK colors (∼900 objects), and the environment was defined as the galaxy density contrast, δ, using the fifth nearest-neighbor approach. The analysis of Σ versus δ was carried out in two stellar mass bins. In galaxies with ℳ ≤ 2 × 1011 M⊙, no correlation between Σ and δ is observed. This implies that the accretion of satellite galaxies, which is more frequent in denser environments (groups or cluster outskirts) and efficient in reducing the galaxy Σ, is not relevant in the formation and evolution of these systems. Conversely, in galaxies with ℳ > 2 × 1011 M⊙, we find an excess of MPGs with low Σ and a deficit of high-Σ MPGs in the densest regions with respect to other environments. We interpret this result as due to the migration of some high-Σ MPGs (< 1% of the total population of MPGs) into low-Σ MPGs, probably through mergers or cannibalism of small satellites. In summary, our results imply that the accretion of satellite galaxies has a marginal role in the mass-assembly history of most MPGs. We have previously found that the number density of VIPERS massive star-forming galaxies (MSFGs) declines rapidily from z = 0.8 to z = 0.5, which mirrors the rapid increase in the number density of MPGs. This indicates that the MSFGs at z ≥ 0.8 migrate to the MPG population. Here, we investigate the Σ–δ relation of MSFGs at z ≥ 0.8 and find that it is consistent within 1σ with that of low-Σ MPGs at z ≤ 0.8. Thus, the results of this and our previous paper show that MSFGs at z ≥ 0.8 are consistent in terms of number and environment with being the progenitors of low-Σ MPGs at z < 0.8.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".