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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".