INTRACLUSTER MEDIUM ENTROPY PROFILES FOR A <i>CHANDRA</i> ARCHIVAL SAMPLE OF GALAXY CLUSTERS
Bibliographic record
Abstract
We present radial entropy profiles of the intracluster medium (ICM) for a collection of 239 clusters taken from the Chandra X-ray Observatory's Data Archive. Entropy is of great interest because it controls ICM global properties and records the thermal history of a cluster. Entropy is therefore a useful quantity for studying the effects of feedback on the cluster environment and investigating any breakdown of cluster self-similarity. We find that most ICM entropy profiles are well fitted by a model which is a power law at large radii and approaches a constant value at small radii: K ( r ) = K 0 + K 100 ( r /100 kpc) α , where K 0 quantifies the typical excess of core entropy above the best-fitting power law found at larger radii. We also show that the K 0 distributions of both the full archival sample and the primary Highest X-Ray Flux Galaxy Cluster Sample of Reiprich (2001) are bimodal with a distinct gap between K 0 ≈ 30–50 keV cm 2 and population peaks at K 0 ∼ 15 keV cm 2 and K 0 ∼ 150 keV cm 2 . The effects of point-spread function smearing and angular resolution on best-fit K 0 values are investigated using mock Chandra observations and degraded entropy profiles, respectively. We find that neither of these effects is sufficient to explain the entropy-profile flattening we measure at small radii. The influence of profile curvature and number of radial bins on best-fit K 0 is also considered, and we find no indication that K 0 is significantly impacted by either. For completeness, we include previously unpublished optical spectroscopy of Hα and [N ii ] emission lines discussed in Cavagnolo et al. (2008a). All data and results associated with this work are publicly available via the project Web site.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".