SUNYAEV-ZEL'DOVICH EFFECT OBSERVATIONS OF STRONG LENSING GALAXY CLUSTERS: PROBING THE OVERCONCENTRATION PROBLEM
Bibliographic record
Abstract
We have measured the Sunyaev–Zel'dovich (SZ) effect for a sample of 10 strong lensing selected galaxy clusters using the Sunyaev–Zel'dovich Array (SZA). The SZA is sensitive to structures on spatial scales of a few arcminutes, while the strong lensing mass modeling constrains the mass at small scales (typically <30''). Combining the two provides information about the projected concentrations of the strong lensing clusters. The Einstein radii we measure are twice as large as expected given the masses inferred from SZ scaling relations. A Monte Carlo simulation indicates that a sample randomly drawn from the expected distribution would have a larger median Einstein radius than the observed clusters about 3% of the time. The implied overconcentration has been noted in previous studies and persists for this sample, even when we take into account that we are selecting large Einstein radius systems, suggesting that the theoretical models still do not fully describe the observed properties of strong lensing clusters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".