Properties of galaxy groups in the Sloan Digital Sky Survey - II. Active galactic nucleus feedback and star formation truncation
Bibliographic record
Abstract
Successfully reproducing the galaxy luminosity function (LF) and the bimodality in the galaxy distribution requires a mechanism that can truncate star formation in massive haloes. Current models of galaxy formation consider two such truncation mechanisms: strangulation, which acts on satellite galaxies, and active galactic nucleus (AGN) feedback, which predominantly affects central galaxies. The efficiencies of these processes set the blue fraction of galaxies, fblue(L, M), as a function of galaxy luminosity, L, and halo mass, M. In this paper, we use a galaxy group catalogue extracted from the Sloan Digital Sky Survey (SDSS) to determine fblue(L, M). To demonstrate the potential power of these data as a benchmark for galaxy formation models, we compare the results to the semi-analytical model for galaxy formation of Croton et al. Although this model accurately fits the global statistics of the galaxy population, as well as the shape of the conditional LF, there are significant discrepancies when the blue fraction of galaxies as a function of mass and luminosity is compared between the observations and the model. In particular, the model predicts (i) too many faint satellites in massive haloes, (ii) a blue fraction of satellites that is much too low, and (iii) a blue fraction of centrals that is too high and with an inverted luminosity dependence. In the same order, we argue that these discrepancies owe to (i) the neglect of tidal stripping in the semi-analytical model, (ii) the oversimplified treatment of strangulation, and (iii) improper modelling of dust extinction and/or AGN feedback. The data presented here will prove useful to test and calibrate future models of galaxy formation and, in particular, to discriminate between various models for AGN feedback and other star formation truncation mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".