The Growth Of Galaxies In Cosmological Simulations
Bibliographic record
Abstract
We use hydrodynamic simulations to examine how the baryonic components of galaxies are assembled, focusing on the relative importance of mergers and smooth accretion in the formation of ~L* systems. In our primary simulation, which models a (50 h-1 Mpc)3 comoving volume of a Λ-dominated cold dark matter universe, the space density of objects at our (64 particle) baryon mass resolution threshold Mc = 5.4 × 1010 M corresponds to that of observed galaxies with L ~ L*/4. Galaxies above this threshold gain most of their mass by accretion rather than by mergers. At the redshift of peak mass growth, z 2, accretion dominates over merging by about 4 : 1. The mean accretion rate per galaxy declines from ~40 M yr-1 at z = 2 to ~10 M yr-1 at z = 0, while the merging rate peaks later (z 1) and declines more slowly, so by z = 0 the ratio is about 2 : 1. We cannot distinguish truly smooth accretion from merging with objects below our mass resolution threshold, but extrapolating our measured mass spectrum of merging objects, dP/dM M-α with α ~ 1, implies that subresolution mergers would add relatively little mass. The global star formation history in these simulations tracks the mass accretion rate rather than the merger rate. At low redshift, destruction of galaxies by mergers is approximately balanced by the growth of new systems, so the comoving space density of resolved galaxies stays nearly constant despite significant mass evolution at the galaxy-by-galaxy level. The predicted merger rate at z 1 agrees with recent estimates from close pairs in the Canada-France Redshift Survey and Canadian Network for Observational Cosmology Redshift Survey.
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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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 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".