Anisotropic Galactic Outflows and Enrichment of the Intergalactic Medium. I. Monte Carlo Simulations
Bibliographic record
Abstract
We have developed an analytical model to describe the evolution of anisotropic galactic outflows. With it we investigate the impact of varying opening angle on galaxy formation and the evolution of the intergalactic medium. We have implemented this model in a Monte Carlo algorithm to simulate galaxy formation and outflows in a cosmological context. Using this algorithm, we have simulated the evolution of a comoving volume of size (12 h -1 Mpc) 3 in the ΛCDM universe. Starting from a Gaussian density field at redshift z = 24, we follow the formation of ~20,000 galaxies and simulate the galactic outflows produced by these galaxies. When these outflows collide with density peaks, ram pressure stripping of the gas inside the peaks may result. This occurs in around half the cases and prevents the formation of galaxies. Anisotropic outflows follow the path of least resistance and thus travel preferentially into low-density regions, away from cosmological structures (filaments and pancakes) in which galaxies form. As a result, the number of collisions is reduced, leading to the formation of a larger number of galaxies. Anisotropic outflows can significantly enrich low-density systems with metals. Conversely, the cross pollution in metals of objects located in a common cosmological structure, like a filament, is significantly reduced. Highly anisotropic outflows can travel across cosmological voids and deposit metals in other, unrelated cosmological structures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".