Simulations of binary galaxy mergers and the link with Fast Rotators, Slow Rotators, and Kinematically Distinct Cores
Bibliographic record
Abstract
We study the formation of early-type galaxies (ETGs) through mergers with a sample of 70 high-resolution numerical simulations of binary mergers of disc galaxies. These simulations encompass various mass ratios, initial conditions and orbital parameters. We find that binary mergers of disc galaxies with mass ratios of 3:1 and 6:1 are nearly always classified as Fast Rotators according to the ATLAS3D criterion: they preserve the structure of the input fast rotating spiral progenitors. Major disc mergers (mass ratios of 2:1 and 1:1) lead to both Fast and Slow Rotators. Most of the Slow Rotators hold a stellar Kinematically Distinct Core (KDC) in their 1-3 central kilo-parsec: these KDCs are built from the stellar components of the progenitors. The mass ratio of the progenitors is a fundamental parameter for the formation of Slow Rotators in binary mergers, but it also requires a retrograde spin for the progenitor galaxies with respect to the orbital angular momentum. The importance of the initial spin of the progenitors is also investigated in the library of galaxy mergers of the GalMer project.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".