THE EMPIRE SURVEY: SYSTEMATIC VARIATIONS IN THE DENSE GAS FRACTION AND STAR FORMATION EFFICIENCY FROM FULL-DISK MAPPING OF M51
Bibliographic record
Abstract
ABSTRACT We present the first results from the EMPIRE survey, an IRAM large program that is mapping tracers of high-density molecular gas across the disks of nine nearby star-forming galaxies. Here, we present new maps of the 3 mm transitions of HCN, HCO+, and HNC across the whole disk of our pilot target, M51. As expected, dense gas correlates with tracers of recent star formation, filling the “luminosity gap” between Galactic cores and whole galaxies. In detail, we show that both the fraction of gas that is dense, f dense traced by HCN/CO, and the rate at which dense gas forms stars, SFE dense traced by IR/HCN, depend on environment in the galaxy. The sense of the dependence is that high-surface-density, high molecular gas fraction regions of the galaxy show high dense gas fractions and low dense gas star formation efficiencies. This agrees with recent results for individual pointings by Usero et al. but using unbiased whole-galaxy maps. It also agrees qualitatively with the behavior observed contrasting our own Solar Neighborhood with the central regions of the Milky Way. The sense of the trends can be explained if the dense gas fraction tracks interstellar pressure but star formation occurs only in regions of high density contrast.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".