Low-redshift Lyman limit systems as diagnostics of cosmological inflows and outflows
Bibliographic record
Abstract
We use cosmological hydrodynamic simulations with stellar feedback from the FIRE (Feedback In Realistic Environments) project to study the physical nature of Lyman limit systems (LLSs) at z ≤ 1. At these low redshifts, LLSs are closely associated with dense gas structures surrounding galaxies, such as galactic winds, dwarf satellites and cool inflows from the intergalactic medium. Our analysis is based on 14 zoom-in simulations covering the halo mass range Mh ≈ 109–1013 M⊙ at z = 0, which we convolve with the dark matter halo mass function to produce cosmological statistics. We find that the majority of cosmologically selected LLSs are associated with haloes in the mass range 1010 ≲ Mh ≲ 1012 M⊙. The incidence and H i column density distribution of simulated absorbers with columns in the range |$10^{16.2} \le N_{\rm H\,\small {I}} \le 2\times 10^{20}$| cm−2 are consistent with observations. High-velocity outflows (with radial velocity exceeding the halo circular velocity by a factor of ≳ 2) tend to have higher metallicities ([X/H] ∼ −0.5) while very low metallicity ([X/H] < −2) LLSs are typically associated with gas infalling from the intergalactic medium. However, most LLSs occupy an intermediate region in metallicity-radial velocity space, for which there is no clear trend between metallicity and radial kinematics. The overall simulated LLS metallicity distribution has a mean (standard deviation) [X/H] = −0.9 (0.4) and does not show significant evidence for bimodality, in contrast to recent observational studies, but consistent with LLSs arising from haloes with a broad range of masses and metallicities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".