Bibliographic record
Abstract
Context. The XXL Survey is the largest survey carried out by XMM-Newton. Covering an area of 50 deg2, the survey contains ~450 galaxy clusters out to a redshift ~2 and to an X-ray flux limit of ~ 5 × 10-15 erg s-1 cm-2. This paper is part of the first release of XXL results focussed on the bright cluster sample. Aims. We investigate the scaling relation between weak-lensing mass and X-ray temperature for the brightest clusters in XXL. The scaling relation discussed in this article is used to estimate the mass of all 100 clusters in XXL-100-GC. Methods. Based on a subsample of 38 objects that lie within the intersection of the northern XXL field and the publicly available CFHTLenS shear catalog, we derive the weak-lensing mass of each system with careful considerations of the systematics. The clusters lie at 0.1 <z< 0.6 and span a temperature range of T ≃ 1−5 keV. We combine our sample with an additional 58 clusters from the literature, increasing the range to T ≃ 1−10 keV. To date, this is the largest sample of clusters with weak-lensing mass measurements that has been used to study the mass-temperature relation. Results. The mass-temperature relation fit (M ∝ Tb) to the XXL clusters returns a slope b = 1.78+0.37-0.32 and intrinsic scatter σlnM|T≃ 0.53; the scatter is dominated by disturbed clusters. The fit to the combined sample of 96 clusters is in tension with self-similarity, b = 1.67 ± 0.12 and σlnM|T ≃ 0.41. Conclusions. Overall our results demonstrate the feasibility of ground-based weak-lensing scaling relation studies down to cool systems of ~1 keV temperature and highlight that the current data and samples are a limit to our statistical precision. As such we are unable to determine whether the validity of hydrostatic equilibrium is a function of halo mass. An enlarged sample of cool systems, deeper weak-lensing data, and robust modelling of the selection function will help to explore these issues further.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.043 |
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".