Power spectrum of the Sunyaev-Zel’dovich effect
Bibliographic record
Abstract
The hot gas in the IGM produces anisotropies in the cosmic microwave background (CMB) through the thermal Sunyaev-Zel'dovich (SZ) effect. The SZ effect is a powerful probe of large-scale structure in the universe, and must be carefully subtracted from measurements of the primary CMB anisotropies. We use moving-mesh hydrodynamical simulations to study the 3-dimensional statistics of the gas, and compute the mean Comptonization parameter y and the angular power spectrum of the SZ fluctuations, for different cosmologies. We compare these results with predictions using the Press-Schechter formalism. We find that the two methods agree approximately, but differ in details. We discuss this discrepancy, and show that resolution limits the reliability of our results to the $200\ensuremath{\lesssim}l\ensuremath{\lesssim}2000$ range. For cluster normalized CDM models, we find a mean y-parameter of the order of ${10}^{\ensuremath{-}6},$ one order of magnitude below the current observational limits from the COBE-FIRAS instrument. For these models, the SZ power spectrum is comparable to the primordial power spectrum around $l=2000.$ It is well below the projected noise for the upcoming MAP satellite, and should thus not be a limitation for this mission. It should be easily detectable with the future Planck Surveyor mission. We show that groups and filaments (kT\ensuremath{\lesssim}5 keV) contribute about 50% of the SZ power spectrum at $l=500.$ About half of the SZ power spectrum on these scales are produced at redshifts $z\ensuremath{\lesssim}0.1,$ and can thus be detected and removed using existing catalogs of galaxies and x-ray 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".