What Drives the Ionized Gas Outflows in Radio-Quiet AGN?
Bibliographic record
Abstract
We review the mechanisms driving the ionized gas outflows in radio-quiet (RQ) AGN. Although it constitutes ∼90% of the AGN population, what drives these outflows in these AGNs remains an open question. High-resolution imaging and integral field unit (IFU) observation is key to spatially resolving these outflows, whereas radio observations are important to comprehend the underlying radiative processes. Radio interferometric observations have detected linear, collimated structures on the hundreds of pc scale in RQ AGN, which may be very similar to the extended radio jets in powerful galaxies. Proper motions measured in some objects are sub-relativistic. Other processes, such as synchrotron radiation from shock-accelerated gas around the outflows could give rise to radio emissions as well. Near the launching region, these outflows may be driven by the thermal energy of the accretion disk and exhibit free–free emission. IFU observations on the other hand have detected evidence of both winds and jets and the outflows driven by them in radio-quiet AGN. Some examples include nearby AGN such as Mrk 1044 and HE 1353-1917. An IFU study of nearby (z <0.06) RQ AGN has found that these outflows may be related to their radio properties on <100 pc scale, rather than their accretion properties. Recent JWST observations of RQ AGN XID 2028 have revealed that radio jets and wind could inflate bubbles, create cavities, and trigger star formation. Future high-resolution multi-wavelength observations and numerical simulations taking account of both jets and winds are hence essential to understand the complex interaction between radio-quiet AGN and the host from sub-pc to kpc scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".