Shaping physical properties of galaxy subtypes in the VIPERS survey: Environment matters
Bibliographic record
Abstract
Aims. This study aims to explore the relation between the physical properties of different galaxy sub-classes, from red passive to blue star-forming, and their environment. Our work is based on the analysis of 31 631 galaxies from the VIMOS Public Extragalactic Redshift Survey (VIPERS), observed at 0.5 < z < 0.9. The unprecedented volume of VIPERS and the wealth of auxiliary derived data allow us to associate sub-classes of the main galaxy populations with their possibly different evolutionary paths. This is the first time such a study is performed with such statistical precision. Methods. We use the results of an unsupervised clustering algorithm to distinguish 11 subclasses of VIPERS galaxies, based on the multi-dimensional feature space, defined by rest-frame UV to NIR colours. We investigate the relationship between the properties of these sub-classes of galaxies and their local environment, defined as the galaxy density contrast, δ, derived from the fifth nearest neighbour technique. Results. We confirm that the galaxy population-density relation is already in place at z ∼ 0.9, with the blue galaxy fraction decreasing with density, compensated by an increase in the red fraction. We demonstrate how the properties of red, green, and blue galaxy subclasses are altered as they assemble into denser regions, and we attempt to interpret this in the context of their evolution. On average, red galaxies in the high-density environment are larger by 28% than those in low-density environments. In particular, we find one group of galaxies, sub-class C3, whose increase of size with time can be explained mainly as the result of mergers; for other red subclasses, mergers would not seem to play the major role (subclass C2) or would play a negligible role (sub-class C1). The properties of the green galaxies (sub-classes C4–6) depend on whether their stellar mass is above or below a transition mass, log(Mstar/M⊙)=10.6. Low-mass green (9.5 ≲ log(Mstar/M⊙)≲10.6) galaxies appear to have grown through secular processes, while in high-mass (10.6 ≲ log(Mstar/M⊙)≲11.5) green galaxies, mass assembly appears to be dominated by mergers. When it comes to blue galaxies, the trend of decreasing fraction with denser environments seen for the group as a whole (sub-classes C7–11) is found to be driven mostly by one (the most numerous) group of galaxies; sub-class C10. These are compact low-mass galaxies with high, specific star formation rates, which are preferentially found in low-density environments. However, the remaining blue galaxies (sub-classes C7–9) are larger and appear in denser environments than galaxies within C10.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".