Bibliographic record
Abstract
The Sunyaev Zel’dovich effect (SZ effect) is a complete probe of ionized baryons, the majority of which are likely hiding in the intergalactic medium. We ran a 512 3 ΛCDM simulation using a moving mesh hydro code to compute the statistics of the thermal and kinetic SZ effect such as the power spectra and measures of non-Gaussianity. The thermal SZ power spectrum has a very broad peak at multipole l ∼ 2000 − 10 4 with temperature fluctuations ∆T ∼ 15µK. The power spectrum is consistent with available observations and suggests a high σ8 ≃ 1.0 and a possible role of non-gravitational heating. The non-Gaussianity is significant and increases the cosmic variance of the power spectrum by a factor of ∼ 5 for l < 6000. We explore optimal driftscan survey strategies for the AMIBA CMB interferometer and their dependence on cosmology. For SZ power spectrum estimation, we find that the optimal sky coverage for a 1000 hours of integration time is several hundred square degrees. One achieves an accuracy better than 40 % in the SZ measurement of power spectrum and an accuracy better than 20 % in the cross correlation with Sloan galaxies for 2000 < l < 5000. For cluster searches, the optimal scan rate is around 280 hours per square degree with a cluster detection rate 1 every 7 hours, allowing for a false positive rate of 20 % and better than 30 % accuracy in the cluster SZ distribution function measurement. Subject headings: Cosmology-theory-simulation-observation: SZ effect, cosmic microwave background, large scale structure, cluster. – 2 – 1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".