Dissecting the thermal Sunyaev-Zeldovich-gravitational lensing cross-correlation with hydrodynamical simulations
Bibliographic record
Abstract
We use the cosmo-OWLS suite of cosmological hydrodynamical simulations, which includes different galactic feedback models, to predict the cross-correlation signal between weak gravitational lensing and the thermal Sunyaev-Zeldovich (tSZ) y -parameter. The predictions are compared to the recent detection reported by van Waerbeke and collaborators. The simulations reproduce the weak lensing-tSZ cross-correlation, ξ y κ (θ), well. The uncertainty arising from different possible feedback models appears to be important on small scales only (0θ ≲ 1 arcmin), while the amplitude of the correlation on all scales is sensitive to cosmological parameters that control the growth rate of structure (such as σ 8 , Ω m and Ω b ). This study confirms our previous claim (in Ma et al.) that a significant proportion of the signal originates from the diffuse gas component in low-mass ( M halo ≲ 10 14 M ⊙ ) clusters as well as from the region beyond the virial radius. We estimate that approximately 20% of the detected signal comes from low-mass clusters, which corresponds to about 30% of the baryon density of the Universe. The simulations also suggest that more than half of the baryons in the Universe are in the form of diffuse gas outside halos (≳ 5 times the virial radius) which is not hot or dense enough to produce a significant tSZ signal or be observed by X-ray experiments. Finally, we show that future high-resolution tSZ-lensing cross-correlation observations will serve as a powerful tool for discriminating between different galactic feedback models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.002 | 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".