A multi-wavelength survey of AGN in the XMM-LSS field
Bibliographic record
Abstract
Aims. We present a sample of candidate quasars selected using the KX-technique. The data cover 0.68 deg2 of the X-ray Multi-Mirror (XMM) Large-Scale Structure (LSS) survey area where overlapping multi-wavelength imaging data permits an investigation of the physical nature of selected sources.Methods. The KX method identifies quasars on the basis of their optical (R and z') to near-infrared photometry and point-like morphology. We combine these data with optical (u*,g', r',i',z') and mid-infrared (3.6-24 μm) wavebands to reconstruct the spectral energy distributions (SEDs) of candidate quasars.Results. Of 93 sources selected as candidate quasars by the KX method, 25 are classified as quasars by the subsequent SED analysis. Spectroscopic observations are available for 12/25 of these sources and confirm the quasar hypothesis in each case. Even more, 90% of the SED-classified quasars show X-ray emission, a property not shared by any of the false candidates in the KX-selected sample. Applying a photometric redshift analysis to the sources without spectroscopy indicates that the 25 sources classified as quasars occupy the interval 0.7 ≤ z ≤ 2.5. The remaining 68/93 sources are classified as stars and unresolved galaxies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".