Gusty, gaseous flows of FIRE: galactic winds in cosmological simulations with explicit stellar feedback
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it