Dense molecular gas: a sensitive probe of stellar feedback models
Bibliographic record
Abstract
We show that the mass fraction of giant molecular cloud (GMC) gas (n ≳ 100 cm−3) in dense (n ≫ 104 cm−3) star-forming clumps, observable in dense molecular tracers (LHCN/LCO(1–0)), is a sensitive probe of the strength and mechanism(s) of stellar feedback, as well as the star formation efficiencies in the most dense gas. Using high-resolution galaxy-scale simulations with pc-scale resolution and explicit models for feedback from radiation pressure, photoionization heating, stellar winds and supernovae (SNe), we make predictions for the dense molecular gas tracers as a function of GMC and galaxy properties and the efficiency of stellar feedback/star formation. In models with weak/no feedback, much of the mass in GMCs collapses into dense subunits, predicting LHCN/LCO(1–0) ratios order-of-magnitude larger than observed. By contrast, models with feedback properties taken directly from stellar evolution calculations predict dense gas tracers in good agreement with observations. Changing the strength or timing of SNe tends to move systems along, rather than off, the LHCN–LCO relation (because SNe heat lower density material, not the high-density gas). Changing the strength of radiation pressure (which acts efficiently in the highest density gas), however, has a much stronger effect on LHCN than on LCO. We show that degeneracies between the strength of feedback, and efficiency of star formation on small scales, can be broken by the combination of dense gas, intermediate-density gas and total star formation rate (SFR) tracers, and favour models where the galaxy-integrated star formation efficiency in dense gas is low. We also predict that the fraction of dense gas (LHCN/LCO(1–0)) increases with increasing GMC surface density; this drives a trend in LHCN/LCO(1–0) with SFR and luminosity which has tentatively been observed. Our results make specific predictions for enhancements in the dense gas tracers in unusually dense environments such as ultraluminous infrared galaxies and galactic nuclei (including the galactic centre).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".