Simulations of galaxy formation in a cosmological volume
Bibliographic record
Abstract
We present results of large N-body-hydrodynamic simulations of galaxy formation. Our simulations follow the formation of galaxies in cubic volumes of side 100Mpc, in two versions of the cold dark matter (CDM) cosmogony: the standard, Omega=1 SCDM model and the flat, Omega=0.3 LCDM model. Over 2000 galaxies form in each of these simulations. We examine the rate at which gas cools and condenses into dark matter halos. This roughly tracks the cosmic star formation rate inferred from observations at various redshifts. Galaxies in the simulations form gradually over time in the hierarchical fashion characteristic of the CDM cosmogony. In the LCDM model, substantial galaxies first appear at z=5 and the population builds up rapidly until z=1 after which the rate of galaxy formation declines as cold gas is consumed and the cooling time of hot gas increases. In both cosmologies, the present-day K-band luminosity function of the simulated galaxies resembles observations. The galaxy autocorrelation functions differ significantly from those of the dark matter. At the present epoch there is little bias in either model between galaxies and dark matter on large scales, but a significant anti-bias on scales of 1Mpc/h and a positive bias on scales of 100kpc/h. The galaxy correlation function evolves little with redshift in the range z=0-3, and depends on the luminosity of the galaxy sample. The projected pairwise velocity dispersion of the galaxies is much lower than that of the dark matter on scales less than 2Mpc/h. Applying a virial mass estimator to the largest galaxy clusters recovers the cluster virial masses in an unbiased way. (Abridged)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".