A search for X-ray absorbed sources in the 3XMM catalogue using photometric redshifts and Bayesian spectral fits
Bibliographic record
Abstract
Since its launch in 1999, the XMM-Newton mission has compiled the largest catalogue of serendipitous X-ray sources, with the 3XMM being the third version of this catalogue. This was possible thanks to the combination of a large effective area (5000 cm2 at 1 keV) and a wide field of view (30 arcmin). The 3XMM-DR6 catalogue contains about 470 000 unique X-ray sources over an area of 982 deg2. A significant fraction of these (100 178 sources) have reliable optical, near-(NIR), and mid-infrared (MIR) counterparts in the SDSS, PANSTARRS, VIDEO, UKIDSS, and WISE surveys. In a previous paper we presented photometric redshifts for these sources using the TPZ machine-learning algorithm. About a quarter of these (22 677) have adequate photon statistics, meaning that a reliable X-ray spectrum can be extracted. Owing to both the X-ray counts selection and the optical counterpart constraint, the sample above is biased towards the bright sources. Here, we present XMMFITCAT-Z: a spectral fit catalogue for these sources using the Bayesian X-ray Analysis technique. To demonstrate the potential of the present catalogue, we comment on the optical and MIR colours of the 765 X-ray absorbed sources with NH > 1022 cm−2. We show that a considerable fraction of X-ray-selected AGNs would not be classified as AGNs following the MIR W1–W2 versus W2 selection criterion. These are AGNs with lower luminosities, where the contribution of the host galaxy to the MIR emission is non-negligible. Only one-third of obscured AGNs in X-rays present red colours or r–W2 > 6. Also, it appears that the r–W2 criterion, often used in the literature for the selection of obscured AGNs, produces very different X-ray absorbed AGN samples compared to the standard X-ray selection criteria.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".