AGN Feedback in Groups and Clusters of Galaxies
Bibliographic record
Abstract
AGN feedback stands for the dramatic impact that a SMBH can make on its environment. It has become an essential element of models that describe the formation and evolution of baryons in massive virialized halos. The baryons' radiative losses in the cores of these halos might lead to massive cooling and vigorous star formation on the order of 10-1000 Msun/yr, whereas observations show that the star formation rates are considerably less. It has now become clear from an observational, theoretical and simulation perspective that the activity of the central SMBH compensates for gas cooling losses and prevents very high star formation rates in massive galaxies, which otherwise would be much brighter than observed today. While AGN feedback is important over a broad range of halo masses, the most massive objects like galaxy groups and clusters truly provide outstanding laboratories for understanding the intrinsic details of AGN feedback. Partly, this is because in the nearby massive objects we can directly see what AGN feedback is doing to its surrounding hot halo in exquisite details, as opposed to less massive systems. Yet another reason is that in the most massive objects, the magnitude of AGN feedback has to be extremely large, providing the most stringent constraints. In a nutshell, the AGN feedback paradigm in groups and clusters postulates that (i) a SMBH in the center of a halo can release a vast amount of energy, (ii) this energy can be intercepted and thermalized by the gaseous atmosphere and (iii) the system self-regulates so that the energy released scales with the properties of the halo. A combination of multi-wavelength observations provides compelling evidence of the AGN feedback importance. Similarly, theoretical arguments suggest that self-regulation might be a natural property of a system consisting of a gaseous atmosphere and a SMBH at the bottom of the potential well.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".