Nebular dust attenuation with the Balmer and Paschen lines based on the MaNGA survey
Bibliographic record
Abstract
Dust attenuations observed by stars and ionized gas are not necessarily the same. The lack of observational constraints on the nebular dust attenuation curve leaves a large uncertainty when correcting nebular dust attenuation with stellar continuum-based attenuation curves. Making use of the DAP catalogs of the MaNGA survey, we investigate the nebular dust attenuation of H <ns0:sc>II</ns0:sc> regions traced by the Balmer and Paschen lines. Based on a simple simulation, we find that star-forming regions on kpc scales favor the classic foreground screen dust model rather than the uniform mixture model. We propose a novel approach to fit the dust attenuation curve using the emission-line fluxes directly. For strong hydrogen recombination lines (e.g., Hγ, Hδ, and Hϵ), the slopes of the nebular attenuation curve can be well determined and are found to be in good agreement with the Fitzpatrick Milky Way extinction curve with an accuracy of ≲4% in terms of the correction factor. However, severe contaminations and/or systematic uncertainties prevent us from obtaining reasonable values of the slopes for weak recombination lines (e.g., the high-order Balmer lines or the Paschen lines). We discuss how the choice of emission line measurement methods affects the results. Our results demonstrate the difficulty of deriving an average nebular dust attenuation curve given the current ground-based emission-line measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".