A new estimator of resolved molecular gas in nearby galaxies
Bibliographic record
Abstract
ABSTRACT A relationship between dust-reprocessed light from recent star formation and the amount of star-forming gas in a galaxy produces a correlation between Wide-field Infrared Survey Explorer (WISE) 12 μm emission and CO line emission. Here, we explore this correlation on kiloparsec scales with CO(1–0) maps from EDGE–CALIFA matched in resolution to WISE 12 μm images. We find strong CO-12 μm correlations within each galaxy and we show that the scatter in the global CO-12 μm correlation is largely driven by differences from galaxy to galaxy. The correlation is stronger than that between star formation rate and H2 surface densities [Σ(H2)]. We explore multivariable regression to predict Σ(H2) in star-forming pixels using the WISE 12 μm data combined with global and resolved galaxy properties, and provide the fit parameters for the best estimators. We find that Σ(H2) estimators that include $\Sigma (\mathrm{12\:\mu m})$ are able to predict Σ(H2) more accurately than estimators that include resolved optical properties instead of $\Sigma (\mathrm{12\:\mu m})$. These results suggest that 12 μm emission and H2 as traced by CO emission are physically connected at kiloparsec scales. This may be due to a connection between polycyclic aromatic hydrocarbon emission and the presence of H2. The best single-property estimator is $\log \frac{\Sigma (\mathrm{H_2})}{\mathrm{M_\odot \:pc^{-2}}} = (0.48 \pm 0.01) + (0.71 \pm 0.01)\log \frac{\Sigma (\mathrm{12\:\mu m})}{\mathrm{L_\odot \:pc^{-2}}}$. This correlation can be used to efficiently estimate Σ(H2) down to at least 1 M⊙ pc−2 in star-forming regions within nearby galaxies.
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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 | 0.008 |
| 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 | 0.000 |
| 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".