HOMERUN: A new approach to photoionization modeling
Bibliographic record
Abstract
We present HOMERUN (Highly Optimized Multi-cloud Emission-line Ratios Using photo-ionizatioN), a new approach to modeling emission lines from photoionized gas that can simultaneously reproduce all observed line intensities from a wide range of ionization levels with high accuracy. Our approach is based on the weighted combination of multiple single-cloud photoionization models, and contrary to previous works, the novelty of our approach consists of using the weights as free parameters of the fit and constraining them with the observed data. One of the main applications of HOMERUN is the accurate determination of gas-phase metallicities, and we show that a critical point is to allow for a variation of the N/O and S/O abundance ratios, as this can significantly improve the quality of the fit and the accuracy of the results. Moreover, our approach provides a major improvement compared to the single-cloud constant-pressure models commonly used in the literature. By using high-quality spectra from the literature of H II regions, where 10 to 20 emission lines (including several auroral lines) are detected with a high signal-to-noise ratio, we show that all lines are reproduced by the model with an accuracy better than 10%. In particular, the model is able to simultaneously reproduce [O I]λλ6300, 6363; [O II]λλ3726, 3729; [O III]λλ4959, 5007; [S II]λλ6717, 6731; and [S III]λλ9069, 9532 emission lines, which to our knowledge is an unprecedented result. Finally, we show that the gas metallicities estimated with our models for HII regions in the Milky Way are in better agreement with the stellar metallicities than the estimates based on the Te method. Overall, our method provides a new accurate tool to estimate the metallicity and the physical conditions of the ionized gas. It can be applied to many different science cases, from HII regions to active galactic nuclei, and wherever there are emission lines from photoionized gas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".