THREE-DIMENSIONAL HYDRODYNAMIC SIMULATIONS OF MULTIPHASE GALACTIC DISKS WITH STAR FORMATION FEEDBACK. I. REGULATION OF STAR FORMATION RATES
Bibliographic record
Abstract
The energy and momentum feedback from young stars has a profound impact on the interstellar medium (ISM), including heating and driving turbulence in the neutral gas that fuels future star formation. Recent theory has argued that this leads to a quasi-equilibrium self-regulated state, and for outer atomic-dominated disks results in the surface density of star formation Σ SFR varying approximately linearly with the weight of the ISM (or midplane turbulent + thermal pressure). We use three-dimensional numerical hydrodynamic simulations to test the theoretical predictions for thermal, turbulent, and vertical dynamical equilibrium, and the implied functional dependence of Σ SFR on local disk properties. Our models demonstrate that all equilibria are established rapidly, and that the expected proportionalities between mean thermal and turbulent pressures and Σ SFR apply. For outer disk regions, this results in , where Σ is the total gas surface density and ρ sd is the midplane density of the stellar disk (plus dark matter). This scaling law arises because ρ sd sets the vertical dynamical time in our models (and outer disk regions generally). The coefficient in the star formation law varies inversely with the specific energy and momentum yield from massive stars. We find proportions of warm and cold atomic gas, turbulent-to-thermal pressure, and mean velocity dispersions that are consistent with solar-neighborhood and other outer disk observations. This study confirms the conclusions of a previous set of simulations, which incorporated the same physics treatment but was restricted to radial-vertical slices through the ISM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".