Does the HCN/CO ratio trace the star-forming fraction of gas? II. Variations in CO and HCN Emissivity
Bibliographic record
Abstract
We model emissivities of the HCN and CO $J=1-0$ transitions using measured properties of clouds found in normal star forming galaxies and more extreme systems. These models are compared with observations of HCN and CO $J=1-0$ transitions. We combine these model emissivities with predictions of gravoturbulent models of star formation, explore the impact of excitation and optical depth on CO and HCN emission, and assess if observed HCN/CO ratios track the fraction of gravitationally-bound dense gas, $f_\mathrm{grav}$, in molecular clouds. Our modeled HCN/CO ratios and emissivities are consistent with measurements from observations. CO emission shows a range of optical depths across different environments, from optically thick in normal galaxies to moderately optically thin in extreme systems. HCN is only moderately optically thick, with significant subthermal excitation in both normal and extreme galaxies. We find an anticorrelation between HCN/CO and $f_\mathrm{grav}$ as predicted by gravoturbulent models of star formation. Instead this ratio tracks gas at moderate densities ($n>10^{3.5}\ \mathrm{cm}^{-3}$), which is below the standard dense gas threshold of $n>10^{4.5}\ \mathrm{cm}^{-3}$. Variations in CO emissivity depend strongly on optical depth, due to variations in the dynamics of the cloud gas. HCN emissivity depends more strongly on excitation, and thus does not directly track variations in CO emissivity. We conclude that a single line ratio, such as HCN/CO, will not consistently track the fraction of gravitationally-bound, star-forming gas if the critical density for star formation varies in molecular clouds. This work highlights important uncertainties that need to be considered when observationally applying an HCN conversion factor in order to estimate the dense (i.e. $n>10^{4.5}\ \mathrm{cm}^{-3}$) gas content in 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 | 0.001 |
| 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 | 0.001 |
| 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".