The invisible AGN catalogue: a mid-infrared–radio selection method for optically faint active galactic nuclei
Bibliographic record
Abstract
A large fraction of active galactic nuclei (AGN) are ‘invisible’ in extant optical surveys due to either distance or dust-obscuration. The existence of this large population of dust-obscured, infrared (IR)-bright AGN is predicted by models of galaxy–supermassive black hole coevolution and is required to explain the observed X-ray and IR backgrounds. Recently, IR colour cuts with Wide-field Infrared Survey Explorer have identified a portion of this missing population. However, as the host galaxy brightness relative to that of the AGN increases, it becomes increasingly difficult to differentiate between IR emission originating from the AGN and from its host galaxy. As a solution, we have developed a new method to select obscured AGN using their 20-cm continuum emission to identify the objects as AGN. We created the resulting invisible AGN catalogue by selecting objects that are detected in AllWISE (mid-IR) and FIRST (20 cm), but are not detected in SDSS (optical) or 2MASS (near-IR), producing a final catalogue of 46 258 objects. 30 per cent of the objects are selected by existing selection methods, while the remaining 70 per cent represent a potential previously unidentified population of candidate AGN that are missed by mid-IR colour cuts. Additionally, by relying on a radio continuum detection, this technique is efficient at detecting radio-loud AGN at z ≥ 0.29, regardless of their level of dust obscuration or their host galaxy's relative brightness.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".