The<i>XMM</i>Large-Scale Structure survey: a well-controlled X-ray cluster sample over the D1 CFHTLS area
Bibliographic record
Abstract
We present the XMM Large-Scale Structure Survey (XMM-LSS) cluster catalogue corresponding to the Canada–France–Hawaii Telescope Legacy Survey D1 area. The list contains 13 spectroscopically confirmed, X-ray selected galaxy clusters over 0.8 deg2 to a redshift of unity and so constitutes the highest density sample of clusters to date. Cluster X-ray bolometric luminosities range from 0.03 to 5 × 1044 erg s−1. In this study, we describe our catalogue construction procedure: from the detection of X-ray cluster candidates to the compilation of a spectroscopically confirmed cluster sample with an explicit selection function. The procedure further provides basic X-ray products such as cluster temperature, flux and luminosity. We detected slightly more clusters with (0.5–2.0 keV) X-ray fluxes of >2 × 10−14 erg s−1 cm−2 than we expected based on expectations from deep ROSAT surveys. We also present the luminosity–temperature relation for our nine brightest objects possessing a reliable temperature determination. The slope is in good agreement with the local relation, yet compatible with a luminosity enhancement for the 0.15 < z < 0.35 objects having 1 < T < 2 keV, a population that the XMM-LSS is identifying systematically for the first time. The present study permits the compilation of cluster samples from XMM images whose selection biases are understood. This allows, in addition to studies of large-scale structure, the systematic investigation of cluster scaling law evolution, especially for low mass X-ray groups which constitute the bulk of our observed cluster population. All cluster ancillary data (images, profiles, spectra) are made available in electronic form via the XMM-LSS cluster data base.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".