Impact of tangled magnetic fields on fossil radio bubbles
Bibliographic record
Abstract
There is growing consensus that feedback from active galactic nuclei (AGN) is the main mechanism responsible for stopping cooling flows in clusters of galaxies. AGN are known to inflate buoyant bubbles that supply mechanical power to the intracluster gas [intracluster medium (ICM)]. High Reynolds number hydrodynamical simulations show that such bubbles get entirely disrupted within 100 Myr, as they rise in cluster atmospheres, which is contrary to observations. This artificial mixing has consequences for models trying to quantify the amount of heating and star formation in cool core clusters of galaxies. It has been suggested that magnetic fields can stabilize bubbles against disruption. We perform magnetohydrodynamical simulations of fossil bubbles in the presence of tangled magnetic fields using the high-order pencil code. We focus on the physically motivated case where thermal pressure dominates over magnetic pressure and consider randomly oriented fields with and without maximum helicity and a case where large-scale external fields drape the bubble. We find that helicity has some stabilizing effect. However, unless the coherence length of magnetic fields exceeds the bubble size, the bubbles are quickly shredded. As observations of Hydra A suggest that length-scale of magnetic fields may be smaller than typical bubble size, this may suggest that other mechanisms, such as viscosity, may be responsible for stabilizing the bubbles. However, since Faraday rotation observations of radio lobes do not constrain large-scale ICM fields well if they are aligned with the bubble surface, the draping case may be a viable alternative solution to the problem. A generic feature found in our simulations is the formation of magnetic wakes where fields are ordered and amplified. We suggest that this effect could prevent evaporation by thermal conduction of cold Hα filaments observed in the Perseus cluster.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".