Probing the cluster pressure profile with thermal Sunyaev–Zeldovich effect and weak lensing cross-correlation
Bibliographic record
Abstract
ABSTRACT We confront the universal pressure profile (UPP) proposed. with the recent measurement of the cross-correlation function of the thermal Sunyaev–Zeldovich (tSZ) effect from Planck and weak gravitational lensing measurement from the Red Cluster Sequence Lensing Survey. By using the halo model, we calculate the prediction of ξy−κ (lensing convergence and Compton-y parameter) and $\xi ^{y-\gamma _{\rm t}}$ (lensing shear and Compton-y parameter) and fit the UPP parameters by using the observational data. We find consistent UPP parameters when fixing the cosmology to either WMAP 9-yr or Planck 2018 best-fitting values. The best constrained parameter is the pressure profile concentration c500 = r500/rs, for which we find $c_{500} = 2.68^{+1.46}_{-0.96}$ (WMAP-9) and $c_{500} = 1.91^{+1.07}_{-0.65}$ (Planck-2018) for the $\xi ^{y-\gamma _t}$ estimator. The shape index for the intermediate radius region α parameter is constrained to $\alpha =1.75^{+1.29}_{-0.77}$ and $\alpha = 1.65^{+0.74}_{-0.5}$ for WMAP-9 and Planck-2018 cosmologies, respectively. Propagating the uncertainties of the UPP parameters to pressure profiles results in a factor of 3 uncertainty in the shape and magnitude. Further investigation shows that most of the signal of the cross-correlation comes from the low-redshift, inner halo profile (r ≤ rvir/2) with halo mass in the range of 1014–$10^{15}{\, {\rm M}_{\odot }}$, suggesting that this is the major regime that constitutes the cross-correlation signal between weak lensing and tSZ.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".