Self-regulated AGN feedback of light jets in cool-core galaxy clusters
Bibliographic record
Abstract
ABSTRACT Heating from active galactic nuclei (AGNs) is thought to stabilize cool-core clusters, limiting star formation and cooling flows. We employ radiative magnetohydrodynamic (MHD) simulations to model light AGN jet feedback with different accretion modes (Bondi–Hoyle–Lyttleton and cold accretion) in an idealized Perseus-like cluster. Independent of the probed accretion model, accretion efficiency, jet density and resolution, the cluster self-regulates with central entropies and cooling times consistent with observed cool-core clusters in this non-cosmological setting. We find that increased jet efficiencies lead to more intermittent jet powers and enhanced star formation rates. Our fiducial low-density jets can easily be deflected by orbiting cold gaseous filaments, which redistributes angular momentum and leads to more extended cold gas distributions and isotropic bubble distributions. In comparison to our fiducial low momentum-density jets, high momentum-density jets heat less efficiently and enable the formation of a persistent cold gas disc perpendicular to the jets that is centrally confined. Cavity luminosities measured from our simulations generally reflect the cooling luminosities of the intracluster medium and correspond to averaged jet powers that are relatively insensitive to short periods of low-luminosity jet injection. Cold gas structures in our MHD simulations with low momentum-density jets generally show a variety of morphologies ranging from discy to very extended filamentary structures. In particular, magnetic fields are crucial to inhibit the formation of unrealistically massive cold gas discs by redistributing angular momentum between the hot and cold phases and by fostering the formation of elongated cold filaments that are supported by magnetic pressure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".