Gusty, gaseous flows of FIRE: galactic winds in cosmological simulations with explicit stellar feedback
Bibliographic record
Abstract
We present an analysis of the galaxy-scale gaseous outflows from the Feedback in Realistic Environments (FIRE) simulations. This suite of hydrodynamic cosmological zoom simulations resolves formation of star-forming giant molecular clouds to z = 0, and features an explicit stellar feedback model on small scales. Our simulations reveal that high-redshift galaxies undergo bursts of star formation followed by powerful gusts of galactic outflows that eject much of the interstellar medium and temporarily suppress star formation. At low redshift, however, sufficiently massive galaxies corresponding to L* progenitors develop stable discs and switch into a continuous and quiescent mode of star formation that does not drive outflows far into the halo. Mass-loading factors for winds in L* progenitors are η ≈ 10 at high redshift, but decrease to η ≪ 1 at low redshift. Although lower values of η are expected as haloes grow in mass over time, we show that the strong suppression of outflows with decreasing redshift cannot be explained by mass evolution alone. Circumgalactic outflow velocities are variable and broadly distributed, but typically range between one and three times the circular velocity of the halo. Much of the ejected material builds a reservoir of enriched gas within the circumgalactic medium, some of which could be later recycled to fuel further star formation. However, a fraction of the gas that leaves the virial radius through galactic winds is never regained, causing most haloes with mass Mh ≤ 1012 M⊙ to be deficient in baryons compared to the cosmic mean by 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".