The XMM-LSS survey: the Class 1 cluster sample over the extended 11 deg2 and its spatial distribution
Bibliographic record
Abstract
This paper presents 52 X-ray bright galaxy clusters selected within the 11 deg2XMM-LSS survey. 51 of them have spectroscopic redshifts (0.05 < z < 1.06), one is identified at zphot = 1.9, and all together make the high-purity ‘Class 1’ (C1) cluster sample of the XMM-LSS, the highest density sample of X-ray-selected clusters with a monitored selection function. Their X-ray fluxes, averaged gas temperatures (median TX = 2 keV), luminosities (median LX, 500 = 5 × 1043 erg s−1) and total mass estimates (median 5 × 1013 h−1 M⊙) are measured, adapting to the specific signal-to-noise regime of XMM-LSS observations. Particular care is taken in deriving the sample selection function by means of realistic simulations reproducing the main characteristics of XMM observations. The redshift distribution of clusters shows a deficit of sources when compared to the cosmological expectations, regardless of whether Wilkinson Microwave Anisotropy Probe-9 or Planck-2013 cosmic microwave background parameters are assumed. This lack of sources is particularly noticeable at 0.4 ≲ z ≲ 0.9. However, after quantifying uncertainties due to small number statistics and sample variance, we are not able to put firm (i.e. >3σ) constraints on the presence of a large void in the cluster distribution. We work out alternative hypotheses and demonstrate that a negative redshift evolution in the normalization of the LX-TX relation (with respect to a self-similar evolution) is a plausible explanation for the observed deficit. We confirm this evolutionary trend by directly studying how C1 clusters populate the LX-TX-z space, properly accounting for selection biases. We also point out that a systematically evolving, unresolved, central component in clusters and groups (AGN contamination or cool core) can impact the classification as extended sources and be partly responsible for the observed redshift distribution. We provide in a table the catalogue of 52 clusters together with their measured properties.
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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 | 0.002 |
| 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) | 0.003 | 0.001 |
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".