Fountains and storms: the effects of AGN feedback and mergers on the evolution of the intracluster medium in the <scp>romulusc</scp> simulation
Bibliographic record
Abstract
ABSTRACT The intracluster medium (ICM) is a multiphase environment, dynamically regulated by active galactic nuclei (AGN), the motions of cluster galaxies, and mergers with other clusters or groups. AGN provide a central heating source capable of preventing runaway cooling flows and quenching star formation, but how they achieve this is still poorly understood. We investigate the effects of AGN feedback and mergers on the ICM using the high-resolution romulusc cosmological simulation of a 1014 M⊙ galaxy cluster. We demonstrate that AGN feedback regulates and quenches star formation in the brightest cluster galaxy gently, without any explosive episodes, and co-exists with a low entropy core with sub-Gyr cooling times. In contrast, the merger disrupts the ICM structure, heating the core and cutting off the supply of low-entropy, infalling gas that until then fuelled the AGN. We find that this removal of the low-entropy phase correlates with the ratio tcool / tff increasing above 30 in the core, matching observations that cooling gas is only found in clusters where this ratio is 5–30. Importantly, we find that evolution in the inner entropy profile and the ratio of cooling to free-fall time-scale are directly connected to the quenching of star formation in the BCG. This is in line with previous results from idealized simulations and confirmed here within a fully cosmological simulation for the first time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.002 | 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".