Mapping AGN winds: A connection between radio-mode AGNs and the AGN feedback cycle
Bibliographic record
Abstract
We present a kinematic analysis based on the large integral field spectroscopy (IFS) dataset of SDSS-IV MaNGA (Sloan Digital Sky Survey/Mapping Nearby Galaxies at Apache Point Observatory; ∼10 000 galaxies). We have compiled a diverse sample of 594 unique active galactic nuclei (AGNs), identified through a variety of independent selection techniques, encompassing radio (1.4 GHz) observations, optical emission-line diagnostics (BPT), broad Balmer emission lines, mid-infrared colors, and hard X-ray emission. We investigated how ionized gas kinematics behave in these different AGN populations through stacked radial profiles of the [O III] 5007 emission-line width across each AGN population. We contrasted AGN populations against each other (and non-AGN galaxies) by matching samples by stellar mass, [O III] 5007 luminosity, morphology, and redshift. We find similar kinematics between AGNs selected by BPT diagnostics compared to broad-line-selected AGNs. We also identify a population of non-AGNs with similar radial profiles as AGNs, indicative of the presence of remnant outflows (or fossil outflows) of past AGN activity. We find that purely radio-selected AGNs display enhanced ionized gas line widths across all radii. This suggests that our radio-selection technique is sensitive to a population in which AGN-driven kinematic perturbations have been active for longer durations (potentially due to recurrent activity) than in purely optically selected AGNs. This connection between radio activity and extended ionized gas outflow signatures is consistent with recent evidence that suggests radio emission (expected to be diffuse) originated due to shocks from outflows. We conclude that different selection techniques can trace different AGN populations not only in terms of energetics but also in terms of AGN evolutionary stages. Our results are important in the context of the AGN duty cycle and highlight integral field unit data’s potential to deepen our knowledge of AGNs and galaxy evolution.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".