Joint Analysis of Cluster Observations. I. Mass Profile of Abell 478 from Combined X‐Ray, Sunyaev‐Zel’dovich, and Weak‐Lensing Data
Bibliographic record
Abstract
We provide a new framework for the joint analysis of cluster observations (JACO) using simultaneous fits to X-ray, Sunyaev-Zel'dovich (SZ), and weak-lensing data. Our method fits the mass models simultaneously to all data, provides explicit separation of the gaseous, dark, and stellar components, and—for the first time—allows joint constraints on all measurable physical parameters. JACO includes additional improvements to previous X-ray techniques, such as the treatment of the cluster termination shock and explicit inclusion of the BCG's stellar mass profile. An application of JACO to the rich galaxy cluster Abell 478 shows excellent agreement among the X-ray, lensing, and SZ data. We find that Abell 478 is consistent with a cuspy dark matter profile with inner slope n = 1. Accounting for the stellar mass profile of the BCG allows us to rule out inner dark matter slopes n > 1.1 at the 99% confidence level. At large radii, an r -3 asymptotic slope is preferred over an r -4 behavior. All single power-law dark matter models are ruled out at greater than the 99% confidence level. JACO shows that self-consistent modeling of multiwavelength data can provide powerful constraints on the shape of the dark profile.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".