The dark matter distribution in<i>z</i> ~ 0.5 clusters of galaxies
Bibliographic record
Abstract
The total mass of clusters of galaxies is a key parameter for studing massive halos. It relates to numerous gravitational and baryonic processes at play in the framework of large-scale structure formation, thus rendering its determination both important and challenging. From a sample of the 11 X-ray bright clusters selected from the EXCPRES sample, we investigate the optical and X-ray properties of clusters with respect to their total mass derived from weak gravitational lensing. From multicolor, wide-field imaging obtained with MegaCam at CFHT, we derive the shear profile of each individual cluster of galaxies. We carefully investigate all systematic sources related to the weak lensing mass determination. The weak lensing masses are then compared to the X-ray masses obtained from the analysis of XMM-Newton observations assuming hydrostatic equilibrium. We find good agreement between the two mass proxies although a few outliers with either perturbed morphology or poor quality data prevent deriving robust mass estimates. The weak lensing mass is also correlated with the optical richness and the total optical luminosity, as well as with the X-ray luminosity, to provide scaling relations within the redshift range 0.4 < z < 0.6. These relations are in good agreement with previous works at lower redshifts. For the LX − M relation we combine our sample with two other cluster and group samples from the literature, thus covering two decades in mass and X-ray luminosity, with a regular and coherent correlation between the two physical quantities.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".