AEGIS: Radio and Mid‐Infrared Selection of Obscured AGN Candidates
Bibliographic record
Abstract
The application of multiwavelength selection techniques is crucial for discovering a complete and unbiased set of active galactic nuclei (AGNs). Here, we select a sample of 72 AGN candidates in the extended Groth strip (EGS) using deep radio and mid-infrared (mid-IR) data from the Very Large Array (VLA) and the Spitzer Space Telescope , and analyze their properties across other wavelengths. Only 30% of these sources are detected in deep 200 ks Chandra X-Ray Observatory pointings. The X-ray-detected sources demonstrate moderate obscuration with column densities of N H ≳ 10 22 cm −2 . A stacked image of sources undetected by Chandra shows low levels of X-ray activity, suggesting they may be faint or obscured AGNs. Less than 40% of our sample are selected as AGNs with optical broad lines, mid-IR power laws, or X-ray detections. Thus, if our candidates are indeed AGNs, then the radio/mid-IR selection criteria we use provide a powerful tool for identifying sources missed by other surveys.
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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 | 0.000 | 0.001 |
| 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".