The Physical Drivers and Observational Tracers of CO-to-H2 Conversion Factor Variations in Nearby Barred Galaxy Centers
Bibliographic record
Abstract
The CO-to-H$_2$ conversion factor ($α_\rm{CO}$) is central to measuring the amount and properties of molecular gas. It is known to vary with environmental conditions, and previous studies have revealed lower $α_\rm{CO}$ in the centers of some barred galaxies on kpc scales. To unveil the physical drivers of such variations, we obtained ALMA Band 3, 6, and 7 observations toward the inner 2 kpc of NGC 3627 and NGC 4321 tracing $^{12}$CO, $^{13}$CO, and C$^{18}$O lines on 100 pc scales. Our multi-line modeling and Bayesian likelihood analysis of these datasets reveal variations of molecular gas density, temperature, optical depth, and velocity dispersion, which are among the key drivers of $α_\rm{CO}$. The central 300 pc nuclei in both galaxies show strong enhancement of temperature $T_\rm{k}>100$ K and density $n_\rm{H_2}>10^3$ cm$^{-3}$. Assuming a CO-to-H$_2$ abundance of $3\times10^{-4}$, we derive 4-15 times lower $α_\rm{CO}$ than the Galactic value across our maps, which agrees well with previous kpc-scale measurements. Combining the results with our previous work on NGC 3351, we find a strong correlation of $α_\rm{CO}$ with low-J $^{12}$CO optical depths ($τ_\rm{CO}$), as well as an anti-correlation with $T_\rm{k}$. The $τ_\rm{CO}$ correlation explains most of the $α_\rm{CO}$ variation in the three galaxy centers, whereas changes in $T_\rm{k}$ influence $α_\rm{CO}$ to second order. Overall, the observed line width and $^{12}$CO/$^{13}$CO 2-1 line ratio correlate with $τ_\rm{CO}$ variation in these centers, and thus they are useful observational indicators for $α_\rm{CO}$ variation. We also test current simulation-based $α_\rm{CO}$ prescriptions and find a systematic overprediction, which likely originates from the mismatch of gas conditions between our data and the simulations.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".