The MURALES survey
Bibliographic record
Abstract
We analyze VLT/MUSE observations of 37 radio galaxies from the Third Cambridge catalogue (3C) with redshift < 0.3 searching for nuclear outflows of ionized gas. These observations are part of the MURALES project (a MUse RAdio Loud Emission line Snapshot survey), whose main goal is to explore the feedback process in the most powerful radio-loud AGN. We applied a nonparametric analysis to the [O III] λ 5007 emission line, whose asymmetries and high-velocity wings reveal signatures of outflows. We find evidence of nuclear outflows in 21 sources, with velocities between ∼400 and 1000 km s −1 , outflowing masses of ∼10 5 − 10 7 M ⊙ , and a kinetic energy in the range ∼10 53 − 10 56 erg. In addition, evidence for extended outflows is found in the 2D gas velocity maps of 13 sources of the subclasses of high-excitation (HEG) and broad-line (BLO) radio galaxies, with sizes between 0.4 and 20 kpc. We estimate a mass outflow rate in the range 0.4–30 M ⊙ yr −1 and an energy deposition rate of Ė kin ∼ 10 42 − 10 45 erg s −1 . Comparing the jet power, the nuclear luminosity of the active galactic nucleus, and the outflow kinetic energy rate, we find that outflows of HEGs and BLOs are likely radiatively powered, while jets likely only play a dominant role in galaxies with low excitation. The low loading factors we measured suggest that these outflows are driven by momentum and not by energy. Based on the gas masses, velocities, and energetics involved, we conclude that the observed ionized outflows have a limited effect on the gas content or the star formation in the host. In order to obtain a complete view of the feedback process, observations exploring the complex multiphase structure of outflows are required.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".