DECONSTRUCTING THERMAL SUNYAEV–ZEL’DOVICH—GRAVITATIONAL LENSING CROSS-CORRELATIONS: IMPLICATIONS FOR THE INTRACLUSTER MEDIUM
Bibliographic record
Abstract
Recent first detections of the cross-correlation of the thermal Sunyaev–Zel'dovich (tSZ) signal in Planck cosmic microwave background (CMB) temperature maps with gravitational lensing maps inferred from the Planck CMB data and the CFHTLenS galaxy survey provide new probes of the relationship between baryons and dark matter. Using cosmological hydrodynamics simulations, we show that these cross-correlation signals are dominated by contributions from hot gas in the intracluster medium (ICM), rather than diffuse, unbound gas located beyond the virial radius (the "missing baryons"). Thus, these cross-correlations offer a tool with which to study the ICM over a wide range of halo masses and redshifts. In particular, we show that the tSZ—CMB lensing cross-correlation is more sensitive to gas in lower-mass, higher-redshift halos and gas at larger cluster-centric radii than the tSZ—galaxy lensing cross-correlation. Combining these measurements with primary CMB data will constrain feedback models through their signatures in the ICM pressure profile. We forecast the ability of ongoing and future experiments to constrain the parameters of a phenomenological ICM model, including the mean amplitude of the pressure–mass relation, the redshift evolution of this amplitude, and the mean outer logarithmic slope of the pressure profile. The results are promising, with ≈5%–20% precision constraints achievable with upcoming experiments, even after marginalizing over cosmological parameters.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".