Gas-phase metallicity of local AGN in the GASP and MaNGA surveys: the role of ram-pressure stripping
Bibliographic record
Abstract
Growing evidence in support of a connection between Active Galactic Nuclei (AGN) activity and the Ram-Pressure Stripping (RPS) phenomenon has been found both observationally and theoretically in the past decades. In this work, we further explore the impact of RPS on the AGN activity by estimating the gas-phase metallicity of nuclear regions and the mass-metallicity relation of galaxies at $z \leq$ 0.07 and with stellar masses $\log {\rm M}_* / {\rm M}_\odot \geq 9.0 $, either experiencing RPS or not. To measure oxygen abundances, we exploit Integral Field Spectroscopy data from the GASP and MaNGA surveys, photoionization models generated with the code CLOUDY and the code Nebulabayes to compare models and observations. In particular, we build CLOUDY models to reproduce line ratios induced by photoionization from stars, AGN, or a contribution of both. We find that the distributions of metallicity and [O III]$λ$5007 luminosity of galaxies undergoing RPS are similar to the ones of undisturbed galaxies. Independently of the RPS, we do not find a correlation between stellar mass and AGN metallicity in the mass range $\log {\rm M}_* / {\rm M}_\odot \geq 10.4$, while for the star-forming galaxies we observe the well-known mass-metallicity relation (MZR) between $ 9.0 \leq \log \ {\rm M}_* /{\rm M}_\odot \leq 10.8$ with a scatter mainly driven by the star-formation rate (SFR) and a plateau around $\log {\rm M}_* / {\rm M}_\odot \sim 10.5$. The gas-phase metallicity in the nuclei of AGN hosts is enhanced with respect to those of SF galaxies by a factor of $\sim$ 0.05 dex regardless of the RPS.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".