Boundless baryons: how diffuse gas contributes to anisotropic tSZ signal around simulated Three Hundred clusters
Bibliographic record
Abstract
ABSTRACT Upcoming advances in galaxy surveys and cosmic microwave background data will enable measurements of the anisotropic distribution of diffuse gas in filaments and superclusters at redshift z = 1 and beyond, observed through the thermal Sunyaev–Zel’dovich (tSZ) effect. These measurements will help distinguish between different astrophysical feedback models, account for baryons that appear to be ‘missing’ from the cosmic census, and present opportunities for using locally anisotropic tSZ statistics as cosmological probes. This study seeks to guide such future measurements by analysing whether diffuse intergalactic gas is a major contributor to anisotropic tSZ signal in The Three Hundred Gizmo-Simba hydrodynamic simulations. We apply multiple different halo boundary and temperature criteria to divide concentrated from diffuse gas at z = 1, then create mock Compton- y maps for the separated components. The maps from 98 simulation snapshots are centred on massive galaxy clusters, oriented by the most prominent filament axis in the galaxy distribution, and stacked. Results vary significantly depending on the definition used for diffuse gas, indicating that assumptions should be clearly defined when claiming observations of the warm-hot intergalactic medium. In all cases, the diffuse gas is important, contributing 25–60 per cent of the tSZ signal in the far field (>4 h−1 comoving Mpc) from the stacked clusters. The gas 1–2 virial radii from halo centres is especially key. Oriented stacking and environmental selections help to amplify the signal from the warm-hot intergalactic medium, which is aligned but less concentrated along the filament axis than the hot halo gas.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".