Why and when is internally driven AGN feedback energetically favoured?
Bibliographic record
Abstract
Active galactic nucleus (AGN) outflows are the heat given up when gas in a galaxy evolves towards thermodynamic equilibrium. Indeed, while AGN feedback regulates the growth of massive galaxies, its origin can be understood as the spontaneous thermodynamic process which ensures that the (Gibbs) free energy of the system always decreases, enabling the galaxy to reach a more energetically favourable state. In particular, it is shown that feedback heating processes will be favoured whenever the hot atmosphere of a galaxy would effectively gain energy as a result of cooling. For example, as the hot atmosphere of a galaxy cools and contracts, the work done by gravity will be thermalized, with a fraction of the gas also being captured by stars and the supermassive black hole at the centre of the galaxy. If this gain of energy exceeds the loss of energy that occurs when cooling gas drops out of the atmosphere, the Gibbs free energy of the system would increase overall. Since this is energetically unfavourable, feedback heating is initiated which acts to reduce the net cooling rate of the atmosphere, thereby preventing any build-up of energy. The Gibbs free energy can also decrease in the absence of feedback heating, but only if the loss of energy due to mass dropping out of the atmosphere exceeds the gains of energy described above. Therefore, to ensure that the Gibbs free energy always decreases, a galaxy will necessarily flip between these two states, experiencing episodes of heating and cooling. Due to the close long-term balance between heating and cooling, the gas in a galaxy will evolve quasistatically towards thermodynamic equilibrium, which has the observable appearance of galaxy growth being regulated by AGN feedback. The same mechanism also provides an explanation for why strong AGN feedback occurs more frequently in cool-core galaxy clusters than in non-cool-core 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 | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".