Bibliographic record
Abstract
The cosmic background due to the Sunyaev–Zeldovich (SZ) effect is expected to be the largest signal at mm and cm wavelengths at a resolution of a few arcminutes. We investigate some simple statistics of SZ maps and their scaling with the normalization of the matter power spectrum, σ8, as well as the effects of the unknown physics of the intracluster medium on these statistics. We show that the SZ background provides a significant background for SZ cluster searches, with the onset of confusion occurring around 1014 h−1 M⊙ in a cosmology-dependent way, where confusion is defined as typical errors in recovered flux larger than 20 per cent. The confusion limit corresponds to the mass at which there are roughly 10 clusters per square degree, with this number nearly independent of cosmology and cluster gas physics. Typical errors grow quickly as lower mass objects are included in the catalogue. We also point out that there is nothing in particular about the rms of the filtered map that makes it especially well suited for capturing aspects of the SZ effect, and other indicators of the one-point SZ probability distribution function are at least as well suited for the task. For example, the full width at half-maximum of the one-point probability distribution has a field-to-field scatter that is about 60 per cent that of the rms. The simplest statistics of SZ maps are largely unaffected by cluster physics such as preheating, although the impact of preheating is clear by eye in the maps. Studies aimed at learning about the physics of the intracluster medium will apparently require more specialized statistical indicators.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.010 | 0.002 |
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".