Disentangling the physical parameters of gaseous nebulae and galaxies
Bibliographic record
Abstract
Abstract We present an analysis to disentangle the connection between physical quantities that characterize the conditions of ionized H ii regions – metallicity (Z), ionization parameter (U), and electron density (ne) – and the global stellar mass (M*) and specific star formation rate (sSFR = SFR/M*) of the host galaxies. We construct composite spectra of galaxies at 0.027 ≤ z ≤ 0.25 from Sloan Digital Sky Survey, separating the sample into bins of M* and sSFR, and estimate the nebular conditions from the emission-line flux ratios. Specially, metallicity is estimated from the direct method based on the faint auroral lines [O iii]λ4363 and [O ii]λλ7320,7330. The derived metallicities cover a range of 12 + log O/H ∼ 7.6–8.9. It is found that the three nebular parameters, Z, U, and ne, are tightly correlated with the location in the M*–sSFR plane. With simple physically motivated ansätze, we derive scaling relations between these physical quantities by performing multiregression analysis. In particular, we find that U is primarily controlled by sSFR, as U∝sSFR0.43, but also depends significantly on both Z and ne. The derived partial dependence of U∝Z−0.36 is weaker than the apparent correlation (U∝Z−1.52). The partial dependence of U on ne is found to be $U \propto n_\mathrm{e}^{-0.29}$. The scaling relations we derived are in agreement with predictions from theoretical models and observations of each aspect of the link between these quantities. Our results provide a useful set of equations to predict the nebular conditions and emission-line fluxes of galaxies in semi-analytic models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".