The ALMOND Survey: Molecular cloud properties and gas density tracers across 25 nearby spiral galaxies with ALMA
Bibliographic record
Abstract
We use new HCN(1-0) data from the ALMOND (ACA Large-sample Mapping Of Nearby galaxies in Dense gas) survey to trace the kpc-scale molecular gas density structure and CO(2-1) data from PHANGS-ALMA to trace the bulk molecular gas across 25 nearby, star-forming galaxies. At 2.1 kpc scale, we measure the density-sensitive HCN/CO line ratio and the SFR/HCN ratio to trace the star formation efficiency in the denser molecular medium. At 150 pc scale, we measure structural and dynamical properties of the molecular gas via CO(2-1) line emission, which is linked to the lower resolution data using an intensity-weighted averaging method. We find positive correlations (negative) of HCN/CO (SFR/HCN) with the surface density, the velocity dispersion and the internal turbulent pressure of the molecular gas. These observed correlations agree with expected trends from turbulent models of star formation, which consider a single free-fall time gravitational collapse. Our results show that the kpc-scale HCN/CO line ratio is a powerful tool to trace the 150 pc scale average density distribution of the molecular clouds. Lastly, we find systematic variations of the SFR/HCN ratio with cloud-scale molecular gas properties, which are incompatible with a universal star formation efficiency. Overall, these findings show that mean molecular gas density, molecular cloud properties and star formation are closely linked in a coherent way, and observations of density-sensitive molecular gas tracers are a useful tool to analyse these variations, linking molecular gas physics to stellar output across galaxy discs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".