ON THE CLUSTER PHYSICS OF SUNYAEV-ZEL'DOVICH AND X-RAY SURVEYS. I. THE INFLUENCE OF FEEDBACK, NON-THERMAL PRESSURE, AND CLUSTER SHAPES ON<i>Y</i>-<i>M</i>SCALING RELATIONS
Bibliographic record
Abstract
The utility of large Sunyaev–Zel'dovich (SZ) surveys for determining cosmological parameters from cluster abundances is limited by the theoretical uncertainties in the integrated SZ–flux-to-mass relation, Y – M . We explore how non-thermal pressure and the anisotropic shape of the gas distribution of the intracluster medium (ICM) impacts Y – M scaling using a suite of smoothed particle hydrodynamic simulations of the cosmic web. We contrast results for models with different treatments of entropy injection and transport, varying radiative cooling, star formation and accompanying supernova feedback, cosmic rays, and energetic feedback from active galactic nuclei (AGNs). We find that the gas kinetic-to-thermal pressure ratio, P kin / P th , from internal bulk motions depends on the cluster mass, and increases in the outer-cluster due to enhanced substructure, as does the asphericity of the ICM gas. With only a ∼5%–10% correction to projected (observable) ellipticities, we can infer the three-dimensional ellipticities. Our simulated Y – M slope roughly follows the self-similar prediction, except for a steepening due to a deficit of gas in lower mass clusters at low redshift in our AGN feedback simulations. AGN feedback enhances the overall Y – M scatter, from ∼11% to ∼13% ( z = 0) and to ∼15% ( z = 1), a reflection of the accretion history variations due to cluster merging. If we split the cluster system into lower, middle, and upper bands of both P kin / P th and long-to-short axis ratio, we find a ∼10% effect on Y – M . Identifying observable second parameters related to internal bulk flows and anisotropy for cluster selection to minimize Y – M scatter in a "fundamental plane" would allow tighter cosmological parameter constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".