THE CO-TO-H<sub>2</sub>CONVERSION FACTOR FROM INFRARED DUST EMISSION ACROSS THE LOCAL GROUP
Bibliographic record
Abstract
We estimate the conversion factor relating CO emission to H 2 mass, α CO , in five Local Group galaxies that span approximately an order of magnitude in metallicity—M 31, M 33, the Large Magellanic Cloud (LMC), NGC 6822, and the Small Magellanic Cloud (SMC). We model the dust mass along the line of sight from infrared (IR) emission and then solve for the α CO that best allows a single gas-to-dust ratio (δ GDR ) to describe each system. This approach remains sensitive to CO-dark envelopes H 2 surrounding molecular clouds. In M 31, M 33, and the LMC we find α CO ≈ 3–9 M ☉ pc −2 (K km s −1 ) −1 , consistent with the Milky Way value within the uncertainties. The two lowest metallicity galaxies in our sample, NGC 6822 and the SMC (12 + log (O/H) ≈ 8.2 and 8.0), exhibit a much higher α CO . Our best estimates are α NGC6822 CO ≈ 30 M ☉ pc −2 (K km s −1 ) −1 and α SMC CO ≈ 70 M ☉ pc −2 (K km s −1 ) −1 . These results are consistent with the conversion factor becoming a strong function of metallicity around 12 + log (O/H) ∼ 8.4–8.2. We favor an interpretation where decreased dust shielding leads to the dominance of CO-free envelopes around molecular clouds below this metallicity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".