Calibrating mid-infrared emission as a tracer of obscured star formation on H II-region scales in the era of JWST
Bibliographic record
Abstract
Measurements of the star formation activity on cloud scales are fundamental to uncovering the physics of the molecular cloud, star formation, and stellar feedback cycle in galaxies. Infrared (IR) emission from small dust grains and polycyclic aromatic hydrocarbons (PAHs) is widely used to trace the obscured component of star formation. However, the relation between these emission features and dust attenuation is complicated by the combined effects of dust heating from old stellar populations and an uncertain dust geometry with respect to heating sources. We used images obtained with NIRCam and MIRI as part of the PHANGS–JWST survey to calibrate the IR emission at 21 μm, and the emission in the PAH-tracing bands at 3.3, 7.7, 10, and 11.3 μm as tracers of obscured star formation. We analysed ∼20 000 optically selected H II regions across 19 nearby star-forming galaxies, and benchmarked their IR emission against dust attenuation measured from the Balmer decrement. We modelled the extinction-corrected H α flux as the sum of the observed H α emission and a term proportional to the IR emission, with a IR as the proportionality coefficient. A constant a IR leads to an extinction-corrected H α estimate that agrees with those obtained with the Balmer decrement with a scatter of ∼0.1 dex for all bands considered. Among these bands, 21 μm emission is demonstrated to be the best tracer of dust attenuation. The PAH-tracing bands underestimate the correction for bright H II regions, since in these environments the ratio of PAH-tracing bands to 21 μm decreases, signalling destruction of the PAH molecules. For fainter H II regions, all bands suffer from an increasing contamination from the diffuse IR background. We present calibrations that take this effect into account by adding an explicit dependence on 2 μm emission or stellar mass surface density.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".