The host galaxies of radio AGN: New views from combining LoTSS and MaNGA observations
Bibliographic record
Abstract
The role of radio mode active galactic nuclei (AGN) feedback on galaxy evolution is still under debate. In this study we utilized a combination of radio continuum observations and optical integral field spectroscopic (IFS) data to explore the impact of radio AGN on the evolution of their host galaxies at global and subgalactic scales. We constructed a comprehensive radio-IFS sample comprising 5548 galaxies with redshift z < 0.15 by cross-matching the LOFAR Two-Metre Sky Survey (LoTSS) with the Mapping Nearby Galaxies at APO (MaNGA) survey. We revisited the tight linear radio continuum–star formation relation and quantify its intrinsic scatter, then used the relation to classify 616 radio-excess AGN with excessive radio luminosities over the values expected from their star formation rates. Massive radio AGN host galaxies are predominantly quiescent systems, but the quenching level shows no correlation with the jet luminosity. The mass assembly histories derived from the stellar population synthesis model fitting agree with the cosmological simulations incorporating radio-mode AGN feedback models. We observe that radio AGN hosts grow faster than a control sample of galaxies matched in stellar mass, and the quenching age (∼5 Gyr) is at larger lookback times than the typical radio jet age (< 1 Gyr). By stacking the spectra in different radial bins and comparing results for radio AGN hosts and their controls, we find emission line excess features in the nuclear region of radio AGN hosts. This excess is more prominent in low-luminosity, low-mass, and compact radio AGN. The [N II ]/H α ratios of the excessive emission line indicate that radio AGN or related jets are ionizing the surrounding interstellar medium in the vicinity of the nucleus. Our results support the scenario that the observed present-day radio AGN activity may help their host galaxies maintain quiescence through gas ionization and heating, but it is not responsible for the past quenching of their hosts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".