The Correlation between WISE 12 μm Emission and Molecular Gas Tracers on Subkiloparsec Scales in Nearby Star-forming Galaxies
Bibliographic record
Abstract
Abstract We complement the MALATANG sample of dense gas in nearby galaxies with archival observations of 12CO and its isotopologues to determine scaling relations between Wide-field Infrared Survey Explorer (WISE) 12 μm emission and molecular gas tracers at subkiloparsec scales. We find that 12 μm luminosity is more tightly correlated with 12CO than it is with 13CO or dense gas tracers. Residuals between predicted and observed 12CO are only weakly correlated with molecular gas mass surface density (Σmol) in regions where Σmol is very low (∼10 M ⊙ pc−2). Above this limit, the 12CO residuals show no correlations with physical conditions of molecular gas, while 13CO residuals depend on the gas optical depth and temperature. By analyzing differences from galaxy to galaxy, we confirm that the 12CO−12 μm relation is strong and statistically robust with respect to star-forming galaxies and active galactic nucleus hosts. These results suggest that WISE 12 μm emission can be used to trace total molecular gas instead of dense molecular gas, likely because polycyclic aromatic hydrocarbons (PAHs, a major contributor to WISE 12 μm emission) may be well mixed with the gas that is traced by 12CO. We propose that WISE 12 μm luminosity can be used to estimate molecular gas surface density for statistical analyses of the star formation process in 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".