Dynamics and Morphology of Cold Gas in Fast, Radiatively Cooling Outflows: Constraining AGN Energetics with Horseshoes
Bibliographic record
Abstract
Abstract Warm ionized and cold neutral outflows with velocities exceeding 100 km s−1 are commonly observed in galaxies and clusters. However, theoretical studies indicate that ram pressure from a hot wind, driven either by the central active galactic nucleus (AGN) or a starburst, cannot accelerate existing cold gas to such high speeds without destroying it. In this work we explore a different scenario, where cold gas forms in a fast, radiatively cooling outflow with temperature T ≲ 107 K. Using 3D hydrodynamic simulations, we demonstrate that cold gas continuously fragments out of the cooling outflow, forming elongated filamentary structures extending tens of kiloparsecs. For a range of physically relevant temperature and velocity configurations, a ring of cold gas perpendicular to the direction of motion forms in the outflow. This naturally explains the formation of transverse cold gas filaments such as the blue loop and the horseshoe filament in the Perseus cluster. Based on our results, we estimate that the AGN outburst responsible for the formation of these two features drove bipolar outflows with velocity >2000 km s−1 and total kinetic energy >8 × 1057 erg about ∼10 Myr ago. We also examine the continuous cooling in the mixing layer between hot and cold gas, and find that radiative cooling only accounts for ∼10% of the total mass cooling rate, indicating that observations of soft X-ray and far-ultraviolet emission may significantly underestimate the growth of cold gas in the cooling flow of galaxy clusters.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".