The uncommon intracluster medium features of the first massive clusters selected independently of their baryon content
Bibliographic record
Abstract
ABSTRACT Our current knowledge of the thermodynamic properties of galaxy clusters comes primarily from detailed studies of clusters selected by their minority components: hot baryons. Most of these studies select the clusters using the component that is being investigated, the intracluster medium (ICM), making the sample choice prone to selection effects. Weak-gravitational lensing allows us to select clusters by the total mass component and, being independent of the type of matter, makes the sample choice unbiased with respect to the baryon content. In this paper, we study four galaxy clusters at intermediate redshift ($0.25\lt z\lt 0.61$), selected from the weak-lensing survey of Miyazaki et al.. We derive core-excised X-ray luminosities, richness-based masses, Compton parameters, and profiles of mass, pressure, and electron densities. These quantities are derived from shear data, Compton maps, and our own X-ray and SZ follow-up. When compared to ICM-selected clusters of the same mass, in the range 2 to $5 \ 10^{14}$ M$_\odot$, our small sample of four clusters is expected to have on average 0.2 rare ($\gt 2\sigma$) features, while we observed on average two rare features in each one of the seven explored properties: richness, core-excised luminosity, Compton parameter, pressure, and electron pressure profiles, and central values of them. The abundance of rare and unique features in such a small sample indicates a fundamental bias in our knowledge of the thermodynamic properties of clusters when derived from ICM-selected samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".