The temperature of the intergalactic medium and the Compton<i>y</i>parameter
Bibliographic record
Abstract
The thermal Sunyaev–Zeldovich (SZ) effect directly probes the thermal energy of the Universe. Its precision modelling and future high-accuracy measurements will provide a powerful way to constrain the thermal history of the Universe. In this paper, we focus on the precision modelling of the gas density weighted temperature and the mean SZ Compton y parameter. We run high-resolution adiabatic hydrodynamic simulations adopting the WMAP cosmology to study the temperature and density distribution of the intergalactic medium (IGM). To quantify possible simulation limitations, we run n=−1, − 2 self-similar simulations. Our analytical model on is based on energy conservation and matter clustering and has no free parameter. Combining both simulations and analytical models thus provides the precision modelling of and . We find that the simulated temperature probability distribution function and shows good convergence. For the WMAP cosmology, our highest-resolution simulation (10243 cells, 100 Mpc h−1 box size) reliably simulates with better than 10 per cent accuracy for z≳ 0.5. Toward z= 0, the simulation mass-resolution effect becomes stronger and causes the simulated to be slightly underestimated (at z= 0, ∼20 per cent underestimated). Since is mainly contributed by the IGM at z≳ 0.5, this simulation effect on is no larger than ∼10 per cent. Furthermore, our analytical model is capable of correcting this artefact. It passes all tests of self-similar simulations and WMAP simulations and is able to predict and to several per cent accuracy. For a low matter density ΛCDM cosmology, the present is 0.32 (σ8/0.84)(Ωm/0.268) keV, which accounts for 10−8 of the critical cosmological density and 0.024 per cent of the cosmic microwave background (CMB) energy. The mean y parameter is 2.6 × 10−6 (σ8/0.84)(Ωm/0.268). The current upper limit of y < 1.5 × 10−5 meured by FIRAS has already ruled out combinations of high σ8≳ 1.1 and high Ωm≳ 0.5.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".