THE CO-TO-H<sub>2</sub>CONVERSION FACTOR AND DUST-TO-GAS RATIO ON KILOPARSEC SCALES IN NEARBY GALAXIES
Bibliographic record
Abstract
We present ∼kiloparsec spatial resolution maps of the CO-to-H 2 conversion factor (α CO ) and dust-to-gas ratio (DGR) in 26 nearby, star-forming galaxies. We have simultaneously solved for α CO and the DGR by assuming that the DGR is approximately constant on kiloparsec scales. With this assumption, we can combine maps of dust mass surface density, CO-integrated intensity, and H i column density to solve for both α CO and the DGR with no assumptions about their value or dependence on metallicity or other parameters. Such a study has just become possible with the availability of high-resolution far-IR maps from the Herschel key program KINGFISH, 12 CO J = (2–1) maps from the IRAM 30 m large program HERACLES, and H i 21 cm line maps from THINGS. We use a fixed ratio between the (2–1) and (1–0) lines to present our α CO results on the more typically used 12 CO J = (1–0) scale and show using literature measurements that variations in the line ratio do not affect our results. In total, we derive 782 individual solutions for α CO and the DGR. On average, α CO = 3.1 M ☉ pc −2 (K km s −1 ) −1 for our sample with a standard deviation of 0.3 dex. Within galaxies, we observe a generally flat profile of α CO as a function of galactocentric radius. However, most galaxies exhibit a lower α CO value in the central kiloparsec—a factor of ∼2 below the galaxy mean, on average. In some cases, the central α CO value can be factors of 5–10 below the standard Milky Way (MW) value of α CO, MW = 4.4 M ☉ pc −2 (K km s −1 ) −1 . While for α CO we find only weak correlations with metallicity, the DGR is well-correlated with metallicity, with an approximately linear slope. Finally, we present several recommendations for choosing an appropriate α CO for studies of 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".