WIDESPREAD AND HIDDEN ACTIVE GALACTIC NUCLEI IN STAR-FORMING GALAXIES AT REDSHIFT >0.3
Bibliographic record
Abstract
We characterize the incidence of active galactic nuclei (AGNs) in 0.3 < z < 1 star-forming galaxies by applying multi-wavelength AGN diagnostics (X-ray, optical, mid-infrared, radio) to a sample of galaxies selected at 70 μm from the Far-Infrared Deep Extragalactic Legacy survey (FIDEL). Given the depth of FIDEL, we detect "normal" galaxies on the specific star formation rate (sSFR) sequence as well as starbursting systems with elevated sSFR. We find an overall high occurrence of AGN of 37% ± 3%, more than twice as high as in previous studies of galaxies with comparable infrared luminosities and redshifts but in good agreement with the AGN fraction of nearby (0.05 < z < 0.1) galaxies of similar infrared luminosities. The more complete census of AGNs comes from using the recently developed Mass-Excitation (MEx) diagnostic diagram. This optical diagnostic is also sensitive to X-ray weak AGNs and X-ray absorbed AGNs, and reveals that absorbed active nuclei reside almost exclusively in infrared-luminous hosts. The fraction of galaxies hosting an AGN appears to be independent of sSFR and remains elevated both on the sSFR sequence and above. In contrast, the fraction of AGNs that are X-ray absorbed increases substantially with increasing sSFR, possibly due to an increased gas fraction and/or gas density in the host galaxies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".