Hidden cooling flows in clusters of galaxies – III. Accretion on to the central black hole
Bibliographic record
Abstract
ABSTRACT Recently, we have uncovered hidden cooling flows (HCFs) in the X-ray spectra of the central brightest galaxies of 11 clusters, 1 group, and 2 elliptical galaxies. Here, we report such flows in a further 15 objects, consisting of 8 clusters, 3 groups, 3 ellipticals, and 1 Red Nugget. The mass cooling rates are about $1\hbox{$\hbox{$\rm \, {\rm M}_{\odot }$}{\rm \, yr}^{-1}\, $}$ in the ellipticals, 2 to $20\hbox{$\hbox{$\rm \, {\rm M}_{\odot }$}{\rm \, yr}^{-1}\, $}$ in the groups, and 20 to $100\hbox{$\hbox{$\rm \, {\rm M}_{\odot }$}{\rm \, yr}^{-1}\, $}$ in regular clusters. The Red Nugget, MRK 1216, has an HCF of $10\hbox{$\hbox{$\rm \, {\rm M}_{\odot }$}{\rm \, yr}^{-1}\, $}$. We review the fate of the cooled gas and investigate how some of it might accrete on to the central black hole. The gas is likely to be very cold and to have fragmented into low-mass stars and smaller objects before being swallowed whole, with little luminous output. If such a scenario is correct and operates at a few $\hbox{$\hbox{$\rm \, {\rm M}_{\odot }$}{\rm \, yr}^{-1}\, $}$ then such objects may host the fastest growing black holes in the low-redshift Universe. We briefly discuss the relevance of HCF to the growth of early galaxies and black holes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".