Tracking down a critical halo mass for killing galaxies through the growth of the red sequence
Bibliographic record
Abstract
Abstract Red-sequence galaxies record the history of terminated star formation in the Universe and can thus provide important clues to the mechanisms responsible for this termination. We construct composite samples of published cluster and field galaxy photometry in order to study the build-up of galaxies on the red sequence, as parametrized by the dwarf-to-giant ratio (DGR). We find that the DGR in clusters is higher than that of the field at all redshifts, implying that the faint end of the red sequence was established first in clusters. We find that the DGR evolves with redshift for both samples, consistent with the ‘down-sizing’ picture of star formation. We examine the predictions of semi-analytic models for the DGR and find that neither the magnitude of its environmental dependence nor its evolution is correctly predicted in the models. Red-sequence DGRs are consistently too high in the models, the most likely explanation being that the strangulation mechanism used to remove hot gas from satellite galaxies is too efficient. Finally, we present a simple toy model including a threshold mass, below which galaxies are not strangled, and show that this can predict the observed evolution of the field DGR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".