Statistics of thermal gas pressure as a probe of cosmology and galaxy formation
Bibliographic record
Abstract
The statistics of thermal gas pressure are a new and promising probe of cosmology and astrophysics. The large-scale cross-correlation between galaxies and the thermal Sunyaev-Zeldovich effect gives the bias-weighted mean electron pressure, ⟨bhPe⟩ . In this paper, we show that ⟨bhPe⟩ is sensitive to the amplitude of fluctuations in matter density, for example ⟨bhPe⟩∝(σ8Ωm0.81h0.67)3.14 at redshift z=0 . We find that at z<0.5 the observed ⟨bhPe⟩ is smaller than that predicted by the state-of-the-art hydrodynamical simulations of galaxy formation, MillenniumTNG, by a factor of 0.93. This can be explained by a lower value of σ8 and Ωm , similar to the so-called “ S8 tension” seen in the gravitational lensing effect, although the influence of astrophysics cannot be completely excluded. The difference between and MillenniumTNG at z<2 is small, indicating that the difference in the galaxy formation models used by these simulations has little impact on ⟨bhPe⟩ at this redshift range. At higher z , we find that both simulations are in a modest tension with the existing upper bounds on ⟨bhPe⟩ . We also find a significant difference between these simulations there, which we attribute to a larger sensitivity to the galaxy formation models in the high redshift regime. Therefore, more precise measurements of ⟨bhPe⟩ at all redshifts will provide a new test of our understanding of cosmology and galaxy formation. Published by the American Physical Society 2024
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".