Exploring the Mass and Redshift Dependencies of the Cluster Pressure Profile with Stacks on Thermal Sunyaev–Zel’dovich Maps
Bibliographic record
Abstract
Abstract We provide novel constraints on the parameters defining the universal pressure profile (UPP) within clusters of galaxies, and explore their dependencies on cluster mass and redshift, from measurements of Sunyaev–Zel’dovich (SZ) Compton y-profiles. We employ both Planck 2015 MILCA and Atacama Cosmology Telescope (ACT) Data Release 4 y-maps over a common ∼2100 deg2 footprint. We combine existing cluster catalogs, based on Kilo Degree Survey, Sloan Digital Sky Survey, and Dark Energy Spectroscopic Instrument Legacy Imaging Surveys observations, for a total of 23,820 clusters, spanning the mass range 1014.0 M ⊙ < M 500 < 1015.1 M ⊙ and the redshift range 0.02 < z < 0.98. We split the clusters into three independent bins in mass and redshift; for each combination, we detect the stacked SZ cluster signal and extract the mean y angular profile. The latter is predicted theoretically by adopting a halo model framework, and a Markov Chain Monte Carlo approach is employed to estimate the UPP parameters, the hydrostatic mass bias b h, and possible cluster miscentering effects. We constrain [P 0, c 500, α, β] to [5.9, 2.0, 1.8, 4.9] with Planck and to [3.8, 1.3, 1.0, 4.4] with ACT, using the full cluster sample, in agreement with previous findings. We do not find any compelling evidence for residual mass or redshift dependencies, thus expanding the validity of the cluster pressure profile over much larger M 500 and z ranges; this is the first time that the model has been tested on such a large (complete and representative) cluster sample. Finally, we obtain loose constraints on the hydrostatic mass bias in the range 0.2–0.3, again in broad agreement with previous works.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".