The rise of active galactic nuclei in the galaxy evolution and assembly semi-analytic model
Bibliographic record
Abstract
ABSTRACT We present a new implementation of the GAlaxy Evolution and Assembly (gaea) semi-analytic model, that features an improved modelling of the process of cold gas accretion on to supermassive black hole (SMBHs), derived from both analytic arguments and high-resolution simulations. We consider different scenarios for the loss of angular momentum required for the available cold gas to be accreted on to the central SMBHs, and we compare different combinations of triggering mechanisms, including galaxy mergers and disc instabilities in star-forming discs. We compare our predictions with the luminosity function (LF) observed for active galactic nuclei (AGNs) and we confirm that a non-instantaneous accretion time-scale (either in the form of a low-angular momentum reservoir or as an assumed light-curve evolution) is needed in order to reproduce the measured evolution of the AGN-LF and the so-called AGN-downsizing trend. Moreover, we also study the impact of AGN feedback, in the form of AGN-driven outflows, on the SF properties of model galaxies, using prescriptions derived both from empirical studies and from numerical experiments. We show that AGN-driven outflows are effective in suppressing the residual star formation rate in massive galaxies (>1011 M⊙) without changing their overall assembly history. These winds also affect the SFR of lower mass galaxies, resulting in a too large fraction of passive galaxies at <1010 M⊙. Finally, we study the Eddington ratio distribution as a function of SMBH mass, showing that only objects more massive than 108 M⊙ are already in a self-regulated state as inferred from observations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".