Bibliographic record
Abstract
We study the clustering properties of metals in the intergalactic medium (IGM) as traced by 619 C iv and 81 Si iv absorption components with N≥ 1012 cm−2 and 316 Mg ii and 82 Fe ii absorption components with N≥ 1011.5 cm−2 in 19 high signal-to-noise ratio (60–100 pixel−1), high-resolution (R = 45 000) quasar spectra. C iv and Si iv trace each other closely and their line-of-sight correlation functions ξ(v) exhibit a steep decline at large separations and a flatter profile below ≈150 km s−1, with a large overall bias. These features do not depend on absorber column densities, although there are hints that the overall amplitude of ξC iv (v) increases with time over the redshift range detected (1.5–3). Carrying out a detailed smoothed particle hydrodynamic simulation (2 × 3203, 57 Mpc3 comoving), we show that the C iv correlation function cannot be reproduced by models in which the IGM metallicity is constant or a local function of overdensity (Z∝Δ2/3). However, the properties of ξC iv(v) are generally consistent with a model in which metals are confined within bubbles with a typical radius Rs about sources of mass ≥Ms. We derive best-fitting values of Rs≈ 2 comoving Mpc and Ms≈ 1012 M⊙ at z = 3. Our lower-redshift (0.5–2) measurements of the Mg ii and Fe ii correlation functions also uncover a steep decline at large separations and a flatter profile at small separations, but the clustering is even higher than in the z = 1.5−3 measurements, and the turnover is shifted to somewhat smaller distances, ≈75 km s−1. Again, these features do not change with column density, but there are hints that the amplitudes of ξMg ii(v) and ξFe ii(v) increase with time. We describe an analytic ‘bubble’ model for these species, which come from regions that are too compact to be accurately simulated numerically, deriving best-fitting values of Rs≈ 2.4 Mpc and Ms≈ 1012 M⊙. Equally good analytic fits to all four species are found in a similarly biased high-redshift enrichment model in which metals are placed within 2.4 comoving Mpc of Ms≈ 3 × 109 sources at z = 7.5.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".