<i>XMM-Newton</i>surveys of the Canada-France Redshift Survey fields - II. The X-ray catalogues, the properties of the host galaxies and the redshift distribution
Bibliographic record
Abstract
We present the X-ray source catalogues for the XMM surveys of the 3-h and 14-h Canada–France Redshift Survey fields (0.5–10 keV flux range ∼2 × 10−15–10−13 erg cm−2 s−1). We use a subset of the XMM sources, which have Chandra positions, to determine the best method of obtaining optical identifications of sources with only XMM positions. We find optical identifications for 79 per cent of the XMM sources for which there are deep optical images. The sources without optical identifications are likely to be optically fainter and have higher redshifts than the sources with identifications. We have estimated ‘photometric redshifts’ for the identified sources, calibrating our method using ∼200 galaxies in the fields with spectroscopic redshifts. We find that the redshift distribution has a strong peak at z∼ 0.7. The host galaxies of active galactic nuclei (AGN) identified in this work cover a wide range of optical properties, with every galaxy type being represented and no obvious preference for one type over another. Redder types tend to be more luminous than blue types, particularly at lower redshifts. The host galaxies also span a wide range of optical luminosity, in contrast to the narrow range found for the starburst galaxies detected in μJy radio surveys. We find a strong correlation between optical and X-ray luminosity similar to the Magorrian relation, although selection effects cannot be ruled out.
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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 | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".