THE INFRARED MEDIUM-DEEP SURVEY. II. HOW TO TRIGGER RADIO AGNs? HINTS FROM THEIR ENVIRONMENTS
Bibliographic record
Abstract
Activity at the centers of galaxies, during which the central supermassive black hole is accreting material, is nowadays accepted to be rather ubiquitous and most probably a phase of every galaxy's evolution. It has been suggested that galactic mergers and interactions may be the culprits behind the triggering of nuclear activity. We use near-infrared data from the new Infrared Medium-Deep Survey and the Deep eXtragalactic Survey of the VIMOS-SA22 field and radio data at 1.4 GHz from the FIRST survey and a deep Very Large Array survey to study the environments of radio active galactic nuclei (AGNs) over an area of ∼25 deg 2 and down to a radio flux limit of 0.1 mJy and a J -band magnitude of 23 mag AB. Radio AGNs are predominantly found in environments similar to those of control galaxies at similar redshift, J -band magnitude, and ( M u − M r ) rest-frame color. However, a subpopulation of radio AGNs is found in environments up to 100 times denser than their control sources. We thus preclude merging as the dominant triggering mechanism of radio AGNs. By fitting the broadband spectral energy distribution of radio AGNs in the least and most dense environments, we find that those in the least dense environments show higher radio-loudness, higher star formation efficiencies, and higher accretion rates, typical of the so-called high-excitation radio AGNs. These differences tend to disappear at z > 1. We interpret our results in terms of a different triggering mechanism for these sources that is driven by mass loss through winds of young stars created during the observed ongoing star formation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".