SHARPENING THE PRECISION OF THE SUNYAEV-ZEL'DOVICH POWER SPECTRUM
Bibliographic record
Abstract
Using both halo model calculations and a large sample of simulated SZ maps, we demonstrate that high-mass clusters add significant non-Gaussian variance to measurements of the SZ power spectrum amplitude. The difficulty in correctly accounting theoretically for the contribution of these objects to the uncertainty in C ℓ leads to a reduced sensitivity to σ 8 . We show that a simple solution is to mask out the brightest clusters in the map before measuring the power spectrum. We demonstrate that fairly conservative masking can reduce the variance and Gaussianize the statistics significantly, thus increasing the sensitivity to cosmological parameters. Choosing which objects to mask is nontrivial; we found that using a fixed sky density produced a well-defined and well-behaved estimate that can easily be applied to real maps. For example, masking the 10 (90) brightest clusters in a 100 deg 2 SZ map will improve the sensitivity to C ℓ by a factor of two at ℓ = 1000 (2000) and 1.5 at ℓ = 2000 (4000). We show that even in the presence of astrophysical foregrounds (primary cosmic microwave background and point sources) and instrument noise, one can increase the precision on measurements of σ 8 by masking up to 0.9 clusters deg −2 .
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".