On the role of supermassive black holes in quenching star formation in local central galaxies
Bibliographic record
Abstract
ABSTRACT We analyse the role of AGN feedback in quenching star formation for massive, central galaxies in the local Universe. In particular, we compare the prediction of two semi-analytic models (l-galaxies and sage) featuring different schemes for AGN feedback, with the SDSS DR7 taking advantage of a novel technique for identifying central galaxies in an observational data set. This enables us to study the correlation between the model passive fractions, which is predicted to be suppressed by feedback from an AGN, and the observed passive fractions in an observationally motivated parameter space. While the passive fractions for observed central galaxies show a good correlation with stellar mass and bulge mass, passive fractions in l-galaxies correlate with the halo and black hole mass. For sage, the passive fraction correlate with the bulge mass as well. Among the two models, sage has a smaller scatter in the black hole–bulge mass $({M_{\rm BH}-M_{\rm Bulge}})$ relation and a slope that agrees better with the most recent observations at z ∼ 0. Despite the more realistic prescription of radio-mode feedback in sage, there are still tensions left with the observed passive fractions and the distribution of quenched galaxies. These tensions may be due to the treatment of galaxies living in non-resolved substructures and the resulting higher merger rates that could bring cold gas which is available for star formation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".