Galaxy bimodality versus stellar mass and environment
Bibliographic record
Abstract
We analyse a z < 0.1 galaxy sample from the Sloan Digital Sky Survey focusing on the variation in the galaxy colour bimodality with stellar mass and projected neighbour density Σ, and on measurements of the galaxy stellar mass functions. The characteristic mass increases with environmental density from about 1010.6 to (Kroupa initial mass function, H0= 70) for Σ in the range 0.1–10 Mpc−2. The galaxy population naturally divides into a red and blue sequence with the locus of the sequences in colour–mass and colour–concentration indices not varying strongly with environment. The fraction of galaxies on the red sequence is determined in bins of 0.2 in log Σ and bins). The red fraction fr generally increases continuously in both Σ and such that there is a unified relation: . Two simple functions are proposed which provide good fits to the data. These data are compared with analogous quantities in semi-analytical models based on the Millennium N-body simulation: the Bower et al. and Croton et al. models that incorporate active galactic nucleus feedback. Both models predict a strong dependence of the red fraction on stellar mass and environment that is qualitatively similar to the observations. However, a quantitative comparison shows that the Bower et al. model is a significantly better match; this appears to be due to the different treatment of feedback in central galaxies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".