The impact of AGN wind feedback in simulations of isolated galaxies with a multiphase ISM
Bibliographic record
Abstract
ABSTRACT Accreting black holes can drive fast and energetic nuclear winds that may be an important feedback mechanism associated with active galactic nuclei (AGN). In this paper, we implement a scheme for capturing feedback from these fast nuclear winds and examine their impact in simulations of isolated disc galaxies. Stellar feedback is modelled using the Feedback In Realistic Environments (fire) physics and produces a realistic multiphase interstellar medium (ISM). We find that AGN winds drive the formation of a low-density, high-temperature central gas cavity that is broadly consistent with analytic model expectations. The effects of AGN feedback on the host galaxy are a strong function of the wind kinetic power and momentum. Low- and moderate-luminosity AGN do not have a significant effect on their host galaxy: the AGN winds inefficiently couple to the ambient ISM and instead a significant fraction of their energy vents in the polar direction. For such massive black holes, accretion near the Eddington limit can have a dramatic impact on the host galaxy ISM: if AGN wind feedback acts for ≳20–30 Myr, the inner ∼1–10 kpc of the ISM is disrupted and the global galaxy star formation rate is significantly reduced. We quantify the properties of the resulting galaxy-scale outflows and find that the radial momentum in the outflow is boosted by a factor of ∼2–3 relative to that initially supplied in the AGN wind for strong feedback scenarios, decreasing below unity for less energetic winds. In contrast to observations, however, the outflows are primarily hot, with very little atomic or molecular gas. We conjecture that merging galaxies and high-redshift galaxies, which have more turbulent and thicker discs and very different nuclear gas geometries, may be even more disrupted by AGN winds than found in our simulations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".