Constraints on the Mass, Concentration, and Nonthermal Pressure Support of Six CLASH Clusters from a Joint Analysis of X-Ray, SZ, and Lensing Data
Bibliographic record
Abstract
Abstract We present a joint analysis of Chandra X-ray observations, Bolocam thermal Sunyaev–Zel’dovich (SZ) effect observations, Hubble Space Telescope (HST) strong-lensing data, and HST and Subaru Suprime-Cam weak-lensing data. The multiwavelength data set is used to constrain parametric models for the distribution of dark and baryonic matter in a sample of six massive galaxy clusters selected from the Cluster Lensing And Supernova survey with Hubble (CLASH). For five of the six clusters, the multiwavelength data set is well described by a relatively simple model that assumes spherical symmetry, hydrostatic equilibrium, and entirely thermal pressure support. The joint analysis yields considerably better constraints on the total mass and concentration of the clusters compared to analysis of any one data set individually. The resulting constraints are consistent with simulation-based predictions for the concentration–mass relation. The subsample of five galaxy clusters is used to place an upper limit on the fraction of pressure support in the intracluster medium (ICM) due to nonthermal processes, such as turbulence and bulk flow of the gas. We constrain the nonthermal pressure fraction at r 500c to be <0.11 at 95% confidence. This is in tension with state-of-the-art hydrodynamical simulations, which predict a nonthermal pressure fraction of ≈0.25 at r 500c for clusters of similar mass and redshift. This tension may be explained by the sample selection and/or our assumption of spherical symmetry.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".