Cosmological galaxy formation simulations using smoothed particle hydrodynamics
Bibliographic record
Abstract
We present the McMaster Unbiased Galaxy Simulations (MUGS), the first nine galaxies of an unbiased selection ranging in total mass from 5 × 1011 M⊙ to 2 × 1012 M⊙ simulated using N-body smoothed particle hydrodynamics at high resolution. The simulations include a treatment of low-temperature metal cooling, UV background radiation, star formation and physically motivated stellar feedback. Mock images of the simulations show that the simulations lie within the observed range of relations such as that between colour and magnitude and that between brightness and circular velocity (Tully–Fisher). The greatest discrepancy between the simulated galaxies and observed galaxies is the high concentration of material at the centre of the galaxies as represented by the centrally peaked rotation curves and the high bulge-to-total ratios of the simulations determined both kinematically and photometrically. This central concentration represents the excess of low angular momentum material that long has plagued morphological studies of simulated galaxies and suggests that higher resolutions and a more accurate description of feedback will be required to simulate more realistic galaxies. Even with the excess central mass concentrations, the simulations suggest the important role merger history and halo spin play in the formation of discs.
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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 | 0.004 |
| 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 | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".