Forming disc galaxies in CDM simulations
Bibliographic record
Abstract
We used fully cosmological, high-resolution N-body + smooth particle hydrodynamic (SPH) simulations to follow the formation of disc galaxies with rotational velocities between 135 and 270 km s−1 in a Λ cold dark matter (CDM) universe. The simulations include gas cooling, star formation, the effects of a uniform ultraviolet (UV) background and a physically motivated description of feedback from supernovae (SNe). The host dark matter haloes have a spin and last major merger redshift typical of galaxy-sized haloes as measured in recent large-scale N-body simulations. The simulated galaxies form rotationally supported discs with realistic exponential scalelengths and fall on both the I band and baryonic Tully–Fisher relations. An extended stellar disc forms inside the Milky Way (MW)-sized halo immediately after the last major merger. The combination of UV background and SN feedback drastically reduces the number of visible satellites orbiting inside a MW-sized halo, bringing it in fair agreement with observations. Our simulations predict that the average age of a primary galaxy's stellar population decreases with mass, because feedback delays star formation in less massive galaxies. Galaxies have stellar masses and current star formation rates as a function of total mass that are in good agreement with observational data. We discuss how both high mass and force resolution and a realistic description of star formation and feedback are important ingredients to match the observed properties of galaxies.
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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 | 0.001 |
| 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 | 0.002 | 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".