Mass Distribution in Galaxy Cluster Cores
Bibliographic record
Abstract
Abstract Many processes within galaxy clusters, such as those believed to govern the onset of thermally unstable cooling and active galactic nucleus feedback, are dependent upon local dynamical timescales. However, accurate mapping of the mass distribution within individual clusters is challenging, particularly toward cluster centers where the total mass budget has substantial radially dependent contributions from the stellar (M *), gas (M gas), and dark matter (M DM) components. In this paper we use a small sample of galaxy clusters with deep Chandra observations and good ancillary tracers of their gravitating mass at both large and small radii to develop a method for determining mass profiles that span a wide radial range and extend down into the central galaxy. We also consider potential observational pitfalls in understanding cooling in hot cluster atmospheres, and find tentative evidence for a relationship between the radial extent of cooling X-ray gas and nebular Hα emission in cool-core clusters. At large radii the entropy profiles of our clusters agree with the baseline power law of K ∝ r 1.1 expected from gravity alone. At smaller radii our entropy profiles become shallower but continue with a power law of the form K ∝ r 0.67 down to our resolution limit. Among this small sample of cool-core clusters we therefore find no support for the existence of a central flat “entropy floor.”
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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 | 0.001 | 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) | 0.003 | 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".