JOINT ANALYSIS OF CLUSTER OBSERVATIONS. II. CHANDRA/XMM-NEWTON X-RAY AND WEAK LENSING SCALING RELATIONS FOR A SAMPLE OF 50 RICH CLUSTERS OF GALAXIES
Bibliographic record
Abstract
We present a study of multiwavelength X-ray and weak lensing scaling relations for a sample of 50 clusters of galaxies. Our analysis combines Chandra and XMM-Newton data using an energy-dependent cross-calibration. After considering a number of scaling relations, we find that gas mass is the most robust estimator of weak lensing mass, yielding 15% ± 6% intrinsic scatter at (the pseudo-pressure Y X yields a consistent scatter of 22% ± 5%). The scatter does not change when measured within a fixed physical radius of 1 Mpc. Clusters with small brightest cluster galaxy (BCG) to X-ray peak offsets constitute a very regular population whose members have the same gas mass fractions and whose even smaller (<10%) deviations from regularity can be ascribed to line of sight geometrical effects alone. Cool-core clusters, while a somewhat different population, also show the same (<10%) scatter in the gas mass–lensing mass relation. There is a good correlation and a hint of bimodality in the plane defined by BCG offset and central entropy (or central cooling time). The pseudo-pressure Y X does not discriminate between the more relaxed and less relaxed populations, making it perhaps the more even-handed mass proxy for surveys. Overall, hydrostatic masses underestimate weak lensing masses by 10% on the average at ; but cool-core clusters are consistent with no bias, while non-cool-core clusters have a large and constant 15%–20% bias between and , in agreement with N -body simulations incorporating unthermalized gas. For non-cool-core clusters, the bias correlates well with BCG ellipticity. We also examine centroid shift variance and power ratios to quantify substructure; these quantities do not correlate with residuals in the scaling relations. Individual clusters have for the most part forgotten the source of their departures from self-similarity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".