Swirls of FIRE: spatially resolved gas velocity dispersions and star formation rates in FIRE-2 disc environments
Bibliographic record
Abstract
ABSTRACT We study the spatially resolved (sub-kpc) gas velocity dispersion (σ)–star formation rate (SFR) relation in the FIRE-2 (Feedback in Realistic Environments) cosmological simulations. We specifically focus on Milky Way-mass disc galaxies at late times (z ≈ 0). In agreement with observations, we find a relatively flat relationship, with σ ≈ 15–30 km s−1 in neutral gas across 3 dex in SFRs. We show that higher dense gas fractions (ratios of dense gas to neutral gas) and SFRs are correlated at constant σ. Similarly, lower gas fractions (ratios of gas to stellar mass) are correlated with higher σ at constant SFR. The limits of the σ–ΣSFR relation correspond to the onset of strong outflows. We see evidence of ‘on-off’ cycles of star formation in the simulations, corresponding to feedback injection time-scales of 10–100 Myr, where SFRs oscillate about equilibrium SFR predictions. Finally, SFRs and velocity dispersions in the simulations agree well with feedback-regulated and marginally stable gas disc (Toomre’s Q = 1) model predictions, and the simulation data effectively rule out models assuming that gas turns into stars at (low) constant efficiency (i.e. 1 per cent per free-fall time). And although the simulation data do not entirely exclude gas accretion/gravitationally powered turbulence as a driver of σ, it appears to be subdominant to stellar feedback in the simulated galaxy discs at z ≈ 0.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".