Upper boundaries of active galactic nucleus regions in optical diagnostic diagrams
Bibliographic record
Abstract
ABSTRACT The distribution of galaxies in optical diagnostic diagrams can provide information about their physical parameters when compared with ionization models under proper assumptions. By using a sample of central emitting regions from the Mapping Nearby Galaxies at Apache Point Observatory survey (MaNGA), we find evidence of the existence of upper boundaries for narrow-line regions (NLRs) of active galactic nuclei (AGNs) in optical Baldwin, Phillips & Terlevich (BPT) diagrams, especially in diagrams involving [S ii]λλ6716, 6731/Hα. Photoionization models can reproduce the boundaries well, as a consequence of the decrease of [S ii]λλ6716, 6731/Hα and [O iii]λ5007/Hβ ratios at very high metallicity. Whilst the exact location of the upper boundary in the [S ii] BPT diagram depends only weakly on the electron density of the ionized cloud and the secondary nitrogen prescription, its dependence on the shapes of the input spectral energy distributions (SEDs) is much stronger. This allows us to constrain the power-law index of the AGN SED between 1 Ryd and ∼100 Ryd to be less than or equal to −1.40 ± 0.05. The coverage of photoionization models in the [N ii] BPT diagram has a stronger dependence on the electron density and the secondary nitrogen prescription. With the density constrained by the [S ii] doublet ratio and the input SED constrained by the [S ii] BPT diagram, we find that the extent of the data in the [N ii] BPT diagram favours those prescriptions with high N/O ratios. Although shock-ionized clouds can produce line ratios similar to those from photoionization, the resulting shapes of the upper boundaries, if they exist, would likely be different from those of photoionizing origin.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".