The duty cycle of radio-mode feedback in complete samples of clusters
Bibliographic record
Abstract
The Chandra X-ray Observatory has revealed X-ray bubbles in the intracluster medium (ICM) of many nearby cooling-flow clusters. The bubbles trace feedback that is thought to couple the central active galactic nucleus (AGN) to the ICM, helping to stabilize cooling flows and govern the evolution of massive galaxies. However, the prevalence and duty cycle of such AGN outbursts is not well understood. To this end, we study how cooling is balanced by bubble heating for complete samples of clusters (the Brightest 55 clusters of galaxies, hereafter B55, and the HIghest X-ray FLUx Galaxy Cluster Sample, HIFLUGCS). We find that the radio luminosity of the central galaxy only exceeds 2.5 × 1030 erg s−1 Hz−1 in cooling-flow clusters. This result implies a connection between the central radio source and the ICM, as expected if AGN feedback is operating. Additionally, we find a duty cycle for radio-mode feedback, the fraction of time that a system possesses bubbles inflated by its central radio source, of ≳69 per cent for the B55 and ≳63 per cent for the HIFLUGCS. These duty cycles are lower limits since some bubbles are likely missed in existing images. We used simulations to constrain the bubble power that might be present and remain undetected in the cooling-flow systems without detected bubbles. Among theses systems, almost all could have significant bubble power. Therefore, our results imply that the duty cycle of AGN outbursts with the potential to heat the gas significantly in cooling-flow clusters is at least 60 per cent and could approach 100 per cent.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".