Diffuse ionized gas and its effects on nebular metallicity estimates of star-forming galaxies
Bibliographic record
Abstract
ABSTRACT We investigate the impact of the diffuse ionized gas (DIG) on abundance determinations in star-forming (SF) galaxies. The DIG is characterized using the H α equivalent width (WH α). From a set of 1 409 SF galaxies from the Mapping Nearby Galaxies at APO (MaNGA) survey, we calculate the fractional contribution of the DIG to several emission lines using high-S/N data from SF spaxels (instead of using noisy emission-lines in DIG-dominated spaxels). Our method is applicable to spectra with observed WH α ≳ 10 Å (which are not dominated by DIG emission). Since the DIG contribution depends on galactocentric distance, we provide DIG-correction formulae for both entire galaxies and single aperture spectra. Applying those to a sample of $\, \gt 90\, 000$ SF galaxies from the Sloan Digital Sky Survey, we find the following. (1) The effect of the DIG on strong-line abundances depends on the index used. It is negligible for the ([O iii]/H β)/([N ii]/H α) index, but reaches ∼0.1 dex at the high-metallicity end for [N ii]/H α. (2) This result is based on the ∼kpc MaNGA resolution, so the real effect of the DIG is likely greater. (3) We revisit the mass–metallicity–star formation rate (SFR) relation by correcting for the DIG contribution in both abundances and SFR. The effect of DIG removal is more prominent at higher stellar masses. Using the [N ii]/Hα index, O/H increases with SFR at high stellar mass, contrary to previous claims.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".