The meaning and consequences of star formation criteria in galaxy models with resolved stellar feedback
Bibliographic record
Abstract
We consider the effects of different criteria for determining where stars will form in gas on galactic scales, in simulations with high (1 pc) resolution, with explicitly resolved physics of Giant Molecular Cloud (GMC) formation and destruction, and stellar feedback from supernovae, radiation pressure, stellar winds and photoheating. We compare: (1) a self-gravity criterion (based on the local virial parameter and the assumption that self-gravitating gas collapses to high density in a single free-fall time), (2) a fixed density threshold, (3) a molecular-gas law, (4) a temperature threshold, (5) a requirement that the gas be Jeans unstable, (6) a criteria that cooling times be shorter than dynamical times and (7) a convergent-flow criterion. We consider all of these in both a Milky Way (MW)-like and high-density (starburst or high-redshift) galaxy. With feedback present, all models produce identical integrated star formation rates (SFRs), in good agreement with the Kennicutt relation; without feedback all produce orders-of-magnitude excessive SFRs. This is totally dependent on feedback and independent of the star formation (SF) law, even if the ‘local’ collapse efficiency is 100 per cent. However, the predicted spatial and density distribution depend strongly on the SF criteria. Because cooling rates are generally fast within galaxy discs, and gas is turbulent, criteria (4)–(7) are very ‘weak’ and spread the SF uniformly over most of the disc (down to densities n ∼ 0.01–0.1 cm−3). A molecular criterion (3) localizes to slightly higher densities, but still a wide range; for metallicity near solar, it is almost identical to a fixed density threshold at n ∼ 1 cm−3 (well below the mean density in the central MW or starburst systems). A fixed density threshold (2) can always select the highest resolved densities, but must be adjusted both for simulation resolution and individual galaxy properties – the same threshold that works well in a MW-like simulation will select nearly all gas in a starburst. Binding criteria (1) tend to adaptively select the largest local overdensities, independent of galaxy model or resolution, and automatically predict clustered SF. We argue that this SF model (possible with other secondary criteria) is most physically motivated and presents significant numerical advantages in simulations with a large dynamic range.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".