A 260 pc resolution ALMA map of HCN(1–0) in the galaxy NGC 4321
Bibliographic record
Abstract
The property of star formation rate (SFR) is tightly connected to the amount of dense gas in molecular clouds. However, it is not fully understood how the relationship between dense molecular gas and star formation varies within galaxies and in different morphological environments. Most previous studies have typically been limited to kiloparsec-scale resolution such that different environments could not be resolved. In this work, we present new ALMA observations of HCN(1−0) at 260 pc scale to test how the amount of dense gas and its ability to form stars varies with environmental properties. Combined with existing CO(2−1) observations from ALMA and Hα from MUSE, we measured the HCN/CO line ratio, a proxy for the dense gas fraction, and SFR/HCN, a proxy for the star formation efficiency of the dense gas. We find a systematic > 1 dex increase (decreases) of HCN/CO (SFR/HCN) towards the centre of the galaxy, and roughly flat trends of these ratios (average variations < 0.3 dex) throughout the disc. While spiral arms, interarm regions, and bar ends show similar HCN/CO and SFR/HCN, on the bar, there is a significantly lower SFR/HCN at a similar HCN/CO. The strong environmental influence on dense gas and star formation in the centre of NGC 4321, suggests either that clouds couple strongly to the surrounding pressure or that HCN emission traces more of the bulk molecular gas that is less efficiently converted into stars. Across the disc, where the ISM pressure is typically low, SFR/HCN is more constant, indicating a decoupling of the clouds from their surrounding environment. The low SFR/HCN on the bar suggests that gas dynamics (e.g. shear and streaming motions) can have a large effect on the efficiency with which dense gas is converted into stars. In addition, we show that HCN/CO is a good predictor of the mean molecular gas surface density at 260 pc scales across environments and physical conditions.
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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 | 0.001 |
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".