A COMPARISON AND JOINT ANALYSIS OF SUNYAEV–ZEL’DOVICH EFFECT MEASUREMENTS FROM PLANCK AND BOLOCAM FOR A SET OF 47 MASSIVE GALAXY CLUSTERS
Bibliographic record
Abstract
ABSTRACT We measure the Sunyaev–Zel’dovich (SZ) signal toward a set of 47 clusters with a median mass of 9.5 × 1014 M ☉ and a median redshift of 0.40 using data from Planck and the ground-based Bolocam receiver. When Planck XMM-like masses are used to set the scale radius , we find consistency between the integrated SZ signal, , derived from Bolocam and Planck based on generalized Navarro, Frenk, and White model fits using A10 shape parameters, with an average ratio of 1.069 ± 0.030 (allowing for the ≃5% Bolocam flux calibration uncertainty). We also perform a joint fit to the Bolocam and Planck data using a modified A10 model with the outer logarithmic slope β allowed to vary, finding β = 6.13 ± 0.16 ± 0.76 (measurement error followed by intrinsic scatter). In addition, we find that the value of β scales with mass and redshift according to . This mass scaling is in good agreement with recent simulations. We do not observe the strong trend of β with redshift seen in simulations, though we conclude that this is most likely due to our sample selection. Finally, we use Bolocam measurements of Y 500 to test the accuracy of the Planck completeness estimate. We find consistency, with the actual number of Planck detections falling approximately 1σ below the expectation from Bolocam. We translate this small difference into a constraint on the effective mass bias for the Planck cluster cosmology results, with .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".