Unveiling AGN outflows: [O <scp>iii</scp>] outflow detection rates and correlation with low-frequency radio emission
Bibliographic record
Abstract
ABSTRACT Some active galactic nuclei (AGN) host outflows which have the potential to alter the host galaxy’s evolution (AGN feedback). These outflows have been linked to enhanced radio emission. Here, we investigate the connection between low-frequency radio emission using the international LOw Frequency ARray (LOFAR) telescope and [O iii] $\lambda$5007 ionized gas outflows using the Sloan Digital Sky Survey. Using the LOFAR Two-metre Sky Survey (LoTSS) Deep Fields, we select 198 AGN with optical spectra, 115 of which are detected at 144 MHz, and investigate their low-frequency radio emission properties. The majority of our sample do not show a radio excess when considering radio luminosity – star formation rate relationship, and are therefore not driven by powerful jets. We extract the [O iii] $\lambda$5007 kinematics and remove AGN luminosity dependencies by matching the radio detected and non-detected AGN in $L_{\mathrm{6\, \mu m}}$ and redshift. Using both spectral fitting and $W_{80}$ measurements, we find radio detected AGN have a higher outflow rate (67.2 $\pm$ 3.4 per cent) than the radio non-detected AGN (44.6 $\pm$ 2.7 per cent), indicating a connection between ionized outflows and the presence of radio emission. For spectra where there are two components of the [O iii] emission line present, we normalize all spectra by the narrow component and find that the average broad component in radio detected AGN is enhanced compared to the radio non-detected AGN. This could be a sign of higher gas content, which is suggestive of a spatial relationship between [O iii] outflows and radio emission in the form of either low-powered jets or shocks from AGN winds.
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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 | 0.000 |
| 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".