RADIO ACTIVE GALAXY NUCLEI IN GALAXY CLUSTERS: HEATING HOT ATMOSPHERES AND DRIVING SUPERMASSIVE BLACK HOLE GROWTH OVER COSMIC TIME
Bibliographic record
Abstract
We estimate the average radio active galactic nucleus (AGN, mechanical) power deposited into the hot atmospheres of galaxy clusters over more than three quarters of the age of the Universe. Our sample was drawn from eight major X-ray cluster surveys and includes 685 clusters in the redshift range 0.1 < z < 0.6 that overlap the area covered by the NRAO VLA Sky Survey (NVSS). The radio-AGN mechanical power was estimated from the radio luminosity of central NVSS sources, using the relation of Cavagnolo et al. that is based on mechanical powers determined from the enthalpies of X-ray cavities. We find only a weak correlation between radio luminosity and cluster X-ray luminosity, although the most powerful radio sources reside in luminous clusters. The average AGN mechanical power of 3 × 10 44 erg s −1 exceeds the X-ray luminosity of 44% of the clusters, indicating that the accumulation of radio-AGN energy is significant in these clusters. Integrating the AGN mechanical power to redshift z = 2.0, using simple models for its evolution and disregarding the hierarchical growth of clusters, we find that the AGN energy accumulated per particle in low luminosity X-ray clusters exceeds 1 keV per particle. This result represents a conservative lower limit to the accumulated thermal energy. The estimate is comparable to the level of energy needed to "preheat" clusters, indicating that continual outbursts from radio-AGN are a significant source of gas energy in hot atmospheres. Assuming an average mass conversion efficiency of η = 0.1, our result implies that the supermassive black holes that released this energy did so by accreting an average of ∼10 9 M ☉ over time, which is comparable to the level of growth expected during the quasar era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".