Ionized Gas Outflows in Low-excitation Radio Galaxies Are Radiation Driven
Bibliographic record
Abstract
Abstract Low-excitation radio galaxies (LERGs) are weakly accreting active galactic nuclei (AGNs) believed to be fueled by radiatively inefficient accretion processes. Despite this, recent works have shown evidence for ionized and neutral hydrogen gas outflows in these galaxies. To investigate the potential drivers of such outflows, we select a sample of 802 LERGs using the Best & Heckman (2012) catalog of radio galaxies. By modeling the [O iii] λ5007 profile in Sloan Digital Sky Survey spectra of a sample of 802 LERGs, we determine that the ionized outflows are present in ∼1.5% of the population. Using 1.4 GHz imaging from the Faint Images of the Radio Sky at Twenty Centimeters survey we analyze the radio morphology of LERGs with outflows and find these to be consistent with the parent LERG population. However, we note that unlike the majority of the LERG population, those LERGs showing outflows have Eddington-scaled accretion rates close to 1%. This is indicative that ionized outflows in LERGs are driven by the radiation pressure from the accretion disk of the AGN rather than the radio jets. We report specific star formation rates in the range of 10−12 < sSFR < 10−9 yr−1. Moreover, we observe higher mass outflow rates, 7–150 M ☉ yr−1, for these LERGs than luminous quasars for a given bolometric luminosity, which could possibly be due to the radio source in LERGs boosting the mass loading. This scenario could indicate that these outflows could potentially drive feedback in LERGs.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".