Batch discovery of nine<i>z</i>∼ 1 clusters using X-ray and<i>K</i>or<i>R</i>,<i>z</i>′ images
Bibliographic record
Abstract
We present the results of an initial search for clusters of galaxies at z ∼ 1 and above, using data from 2.9 square degrees of XMM—Newton images. By selecting weak potentially extended X-ray sources with faint or no identifications in deep, ground-based optical imaging, we have constructed a starting sample of 19 high-redshift cluster candidates. Near-IR and R, z′ imaging of these fields identified nine of them as high-redshift systems. Six of these were confirmed spectroscopically, three at z ∼ 1.0 and the other three in the 0.8 < z < 0.92 range. The remaining three systems have solid photometric evidence to be at zphot ∼ 0.8, 1.0 and 1.3. The present sample significantly increases the number of such clusters. The measured density of z ≳ 1 clusters, after discarding ‘low’-redshift systems at z ≲ 0.92 is about 1.7 deg−2 (with 68 per cent confidence interval equal to [1.0, 2.9]) for fX ≳ 2.5 10−15 erg cm−2 s−1 ([0.5–2] keV) and this is a lower limit, having screened not all potential z ∼ 1 candidate clusters. Coordinates, X-ray measures and evidence for nine X-ray-selected high-redshift clusters is given.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".