ANISOTROPIC ACTIVE GALACTIC NUCLEUS OUTFLOWS AND ENRICHMENT OF THE INTERGALACTIC MEDIUM. II. METALLICITY
Bibliographic record
Abstract
We investigate the large-scale influence of outflows from active galactic nuclei (AGNs) in enriching the intergalactic medium (IGM) with metals in a cosmological context. We combine cosmological simulations of large-scale structure formation with a detailed model of metal enrichment, in which outflows expand anisotropically along the direction of least resistance, distributing metals into the IGM. The metals carried by the outflows are generated by two separate stellar populations: stars located near the central AGN, and stars located in the greater galaxy. Using this algorithm, we performed a series of five simulations of the propagation of AGN-driven outflows in a cosmological volume of size (128 h −1 Mpc) 3 in a ΛCDM universe, and analyze the resulting metal enrichment of the IGM. We found that the metallicity induced in the IGM is greatly dominated by AGNs having bolometric luminosity L >10 9 L ☉ , sources with 10 8 < L / L ☉ < 10 9 having a negligible contribution. Our simulations produced an average IGM metallicity of [O/H] = −5 at z = 5.5, which then rises gradually, and remains relatively flat at a value [O/H] = −2.8 between z = 2 and z = 0. The ejection of metals from AGN host galaxies by AGN-driven outflows is found to enrich the IGM to >10%–20% of the observed values, the number dependent on redshift. The enriched IGM volume fractions are small at z >3, then rise rapidly to the following values at z = 0: 6%–10% of the volume enriched to [O/H]> − 2.5, 14%–24% volume to [O/H]> − 3, and 34%–45% volume to [O/H]> − 4. At z ⩾ 2, there is a gradient of the induced enrichment, the metallicity decreasing with increasing IGM density, enriching the underdense IGM to higher metallicities, a trend more prominent with increasing anisotropy of the outflows. This can explain observations of the metal-enriched low-density IGM at z ∼ 3–4.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".