Major mergers between dark matter haloes – I. Predictions for size, shape, and spin
Bibliographic record
Abstract
The structural properties of individual dark matter haloes, including shape, spin, concentration, and substructure, are linked to the halo’s growth history, but the exact connection between the two is unclear. One open question, in particular, is the effect of major mergers on halo structure. We have performed a large set of simulations of binary equal-mass mergers between isolated haloes with various density profiles, to map out the relationship between the initial conditions and merger parameters and the structure of the final remnant. In this paper we describe our initial set-up and analysis methods, and report on the results for the size, shape, and spin of the merger remnant. The outcomes of mergers are most easily understood in terms of a scaled dimensionless energy parameter κ and an angular momentum (or spin) parameter λ. We find that the axial ratio c/a scales roughly linearly with energy κ while the axial ratio c/b scales linearly with spin λ. Qualitatively, mergers on radial orbits produce prolate remnants, while mergers on tangential orbits produce oblate remnants. The spin of the remnant can be predicted from angular momentum conservation, while the overall size changes as ∼κ−5, as expected from self-similar scaling at constant mean density. We discuss potential cosmological applications for these simple patterns.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".