An optimal and model-independent measurement of the intracluster pressure profile – I. Methodology and first applications
Bibliographic record
Abstract
We present a statistically optimal and model-independent method to extract the pressure profile of hot gas in the intracluster medium (ICM). Using the thermal Sunyaev–Zeldovich effect, we constrain the mean pressure profile of the ICM by appropriately considering all primary cosmic microwave background (CMB) and instrumental noise correlations, while using the maximum resolution and sensitivity of all frequency channels. As a first application, we analyse CMB maps of Wilkinson Microwave Anisotropy Probe 9-year data through a study of the Meta-Catalogue of X-ray Detected Clusters of Galaxies. We constrain the universal pressure profile out to 4R500 with 15σ confidence, though our measurements are only significant out to R200. Using a temperature profile constrained from X-ray observations, we measure the mean gas mass fraction out to R200. Within statistical and systematic uncertainties, our constraints are compatible with the cosmic baryon fraction and the expected gas fraction in haloes. While Planck multifrequency CMB data are expected to reduce statistical uncertainties by a factor of ∼20, we argue that systematic errors in determining mass of clusters dominate the uncertainty in gas mass fraction measurements at the level of ∼20 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".