Measuring chemical abundances in AGN from infrared nebular lines: HII-CHI-M
Bibliographic record
Abstract
Context. Future and ongoing infrared and radio observatories such as JWST, METIS, and ALMA will increase the amount of rest-frame IR spectroscopic data for galaxies by several orders of magnitude. While studies of the chemical composition of the interstellar medium (ISM) based on optical observations have been widely spread over decades for star-forming galaxies (SFGs) and, more recently, for active galactic nuclei (AGN), similar studies need to be performed using IR data. In the case of AGN, this regime can be especially useful given that it is less affected by temperature and dust extinction, traces higher ionic species, and can also provide robust estimations of the chemical abundance ratio N/O. Aims. We present a new tool based on a Bayesian-like methodology (HII-CHI-MISTRY-IR) to estimate chemical abundances from IR emission lines in AGN. We use a sample of 58 AGN with IR spectroscopic data retrieved from the literature, composed by 43 Seyferts, eight ultraluminous infrared galaxies (ULIRGs), four luminous infrared galaxies (LIRGs), and three low-ionization nuclear emission line regions (LINERs), to probe the validity of our method. The estimations of the chemical abundances based on IR lines in our sample are later compared with the corresponding abundances derived from the optical emission lines in the same objects. Methods. HII-CHI-MISTRY-IR takes advantage of photoionization models, characterized by the chemical abundance ratios O/H and N/O, and the ionization parameter U, to compare their predicted emission-line fluxes with a set of observed values. Instead of matching single emission lines, the code uses some specific emission-line ratios that are sensitive to the above free parameters. Results. We report mainly solar and also subsolar abundances for O/H in the nuclear region for our sample of AGN, whereas N/O clusters are around solar values. We find a discrepancy between the chemical abundances derived from IR and optical emission lines, the latter being higher than the former. This discrepancy, also reported by previous studies of the composition of the ISM in AGN from IR observations, is independent of the gas density or the incident radiation field to the gas, and it is likely associated with dust obscuration and/or temperature stratification within the gas nebula.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".