Neutral hydrogen in galaxy haloes at the peak of the cosmic star formation history
Bibliographic record
Abstract
We use high-resolution cosmological zoom-in simulations from the FIRE (Feedback in Realistic Environments) project to make predictions for the covering fractions of neutral hydrogen around galaxies at z = 2–4. These simulations resolve the interstellar medium of galaxies and explicitly implement a comprehensive set of stellar feedback mechanisms. Our simulation sample consists of 16 main haloes covering the mass range Mh ≈ 109–6 × 1012 M⊙ at z = 2, including 12 haloes in the mass range Mh ∼ 1011–1012 M⊙ corresponding to Lyman break galaxies (LBGs). We process our simulations with a ray tracing method to compute the ionization state of the gas. Galactic winds increase the H i covering fractions in galaxy haloes by direct ejection of cool gas from galaxies and through interactions with gas inflowing from the intergalactic medium. Our simulations predict H i covering fractions for Lyman limit systems (LLSs) consistent with measurements around z ∼ 2–2.5 LBGs; these covering fractions are a factor ∼2 higher than our previous calculations without galactic winds. The fractions of H i absorbers arising in inflows and in outflows are on average ∼50 per cent but exhibit significant time variability, ranging from ∼10 to ∼90 per cent. For our most massive haloes, we find a factor ∼3 deficit in the LLS covering fraction relative to what is measured around quasars at z ∼ 2, suggesting that the presence of a quasar may affect the properties of halo gas on ∼100 kpc scales. The predicted covering fractions, which decrease with time, peak at Mh ∼ 1011–1012 M⊙, near the peak of the star formation efficiency in dark matter haloes. In our simulations, star formation and galactic outflows are highly time dependent; H i covering fractions are also time variable but less so because they represent averages over large areas.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".