SIMULATIONS OF THE SUNYAEV-ZEL'DOVICH POWER SPECTRUM WITH ACTIVE GALACTIC NUCLEUS FEEDBACK
Bibliographic record
Abstract
We explore how radiative cooling, supernova feedback, cosmic rays, and a new model of the energetic feedback from active galactic nuclei (AGNs) affect the thermal and kinetic Sunyaev–Zel'dovich (SZ) power spectra. To do this, we use a suite of hydrodynamical TreePM-SPH simulations of the cosmic web in large periodic boxes and tailored higher resolution simulations of individual galaxy clusters. Our AGN feedback simulations match the recent universal pressure profile and cluster mass scaling relations of the REXCESS X-ray cluster sample better than previous analytical or numerical approaches. For multipoles ℓ ≲ 2000, our power spectra with and without enhanced feedback are similar, suggesting that theoretical uncertainties over that range are relatively small, although current analytic and semi-analytic approaches overestimate this SZ power. We find the power at high 2000–10, 000 multipoles in which the Atacama Cosmology Telescope (ACT) and South Pole Telescope (SPT) probe is sensitive to the feedback prescription, and hence can constrain the theory of intracluster gas, in particular for the highly uncertain redshifts >0.8. The apparent tension between σ 8 from primary cosmic microwave background power and from analytic SZ spectra inferred using ACT and SPT data is lessened with our AGN feedback spectra.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".