Cosmological Simulations of the Intergalactic Medium Evolution. III. SPH Simulations
Bibliographic record
Abstract
Abstract We have developed a new numerical algorithm to study the joint evolution of galaxies and the intergalactic medium (IGM) in a cosmological context, with the specific goal of studying the deposition and dispersion of metals in the IGM. This algorithm combines a standard gasdynamical algorithm to simulate the evolution of the IGM, a semi-analytical model to describe the evolution of galaxies, and prescriptions for galaxy formation, accretion, mergers, and tidal disruption. The main goal in designing this algorithm was performance. In its current version, the algorithm can simulate the evolution of cosmological volumes containing thousands of galaxies in a few days, using between 12 and 32 processors. This algorithm is particularly suited for parameter surveys (both numerical parameters and physical parameters) since a large number of simulations can be completed in a fairly short amount of time. Furthermore, the algorithm provides a platform for the development and testing of new treatments of subgrid physics, which could then be implemented into other algorithms. In this paper, we describe the algorithm and present, for illustration, two simulations of the evolution of a (20 Mpc)3 cosmological volume containing ∼1200 galaxies at z = 0.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".