The role of cold flows and reservoirs in galaxy formation with strong feedback
Bibliographic record
Abstract
We examine gas accretion and subsequent star formation in representative galaxies from the McMaster Unbiased Galaxy Simulations. Accreted gas is bimodal with a natural temperature division at 105 K, near the peak of the cooling curve. Cold-mode accretion dominates inflows at early times, creating a peak in total accretion at redshift z = 2–4 and declining exponentially below z ∼ 2. Hot-mode accretion peaks near z = 1–2 and declines gradually. Hot-mode exceeds cold-mode accretion at z ∼ 1.8 for all four galaxies rather than when the galaxy reaches a characteristic mass. Cold-mode accretion can fuel immediate star formation, while hot-mode accretion preferentially builds a large, hot gas reservoir in the halo. Late-time star formation relies on reservoir gas accreted 2–8 Gyr prior. Thus, the reservoir allows the star formation rate to surpass the current overall gas accretion rate. Stellar feedback cycles gas from the interstellar medium back into the hot reservoir. Stronger feedback results in more gas cycling, gas removal in a galactic outflow and less star formation overall, enabling simulations to match the observed star formation history. For lower mass galaxies in particular, strong feedback can delay the star formation peak to z = 1–2 from the accretion peak at z = 2–4.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".