AEGIS: DEMOGRAPHICS OF X-RAY AND OPTICALLY SELECTED ACTIVE GALACTIC NUCLEI
Bibliographic record
Abstract
We develop a new diagnostic method to classify galaxies into active galactic nucleus (AGN) hosts, star-forming galaxies, and absorption-dominated galaxies by combining the [O iii ]/Hβ ratio with rest-frame U − B color. This can be used to robustly select AGNs in galaxy samples at intermediate redshifts ( z < 1). We compare the result of this optical AGN selection with X-ray selection using a sample of 3150 galaxies with 0.3 < z < 0.8 and I AB < 22, selected from the DEEP2 Galaxy Redshift Survey and the All-wavelength Extended Groth Strip International Survey. Among the 146 X-ray sources in this sample, 58% are classified optically as emission-line AGNs, the rest as star-forming galaxies or absorption-dominated galaxies. The latter are also known as "X-ray bright, optically normal galaxies" (XBONGs). Analysis of the relationship between optical emission lines and X-ray properties shows that the completeness of optical AGN selection suffers from dependence on the star formation rate and the quality of observed spectra. It also shows that XBONGs do not appear to be a physically distinct population from other X-ray detected, emission-line AGNs. On the other hand, X-ray AGN selection also has strong bias. About 2/3 of all emission-line AGNs at L bol > 10 44 erg s -1 in our sample are not detected in our 200 ks Chandra images, most likely due to moderate or heavy absorption by gas near the AGN. The 2–7 keV detection rate of Seyfert 2s at z ∼ 0.6 suggests that their column density distribution and Compton-thick fraction are similar to that of local Seyferts. Multiple sample selection techniques are needed to obtain as complete a sample as possible.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".