A radio polarimetric study to disentangle AGN activity and star formation in Seyfert galaxies
Bibliographic record
Abstract
ABSTRACT To understand the origin of radio emission in radio-quiet active galactic nucleus (AGN) and differentiate between the contributions from star formation, AGN accretion, and jets, we have observed a nearby sample of Seyfert galaxies along with a comparison sample of starburst galaxies using the Expanded Very Large Array (EVLA) in full-polarization mode in the B-array configuration. The radio morphologies of the Seyfert galaxies show lobe/bubble-like features or prominent cores in radio emission, whereas the starburst galaxies show radio emission spatially coincident with the star-forming regions seen in optical images. There is tentative evidence that Seyferts tend to show more polarized structures than starburst galaxies at the resolution of our observations. We find that unlike a sample of Seyfert galaxies hosting kilo-parsec scale radio (KSR) emission, starburst galaxies with superwinds do not show radio-excess compared to the radio–FIR correlation. This suggests that shock acceleration is not adequate to explain the excess radio emission seen in Seyferts and hence most likely have a jet-related origin. We also find that the [O iii] luminosity of the Seyferts is correlated with the off-nuclear radio emission from the lobes, whereas it is not well correlated with the total emission which also includes the core. This suggests strong jet–medium interaction, which in turn limits the jet/lobe extents in Seyferts. We find that the power contribution of AGN jet, AGN accretion, and star formation is more or less comparable in our sample of Seyfert galaxies. We also find indications of episodic AGN activity in many of our Seyfert 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 | 0.000 |
| 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) | 0.001 | 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".