Bibliographic record
Abstract
Abstract We use a large cosmological N-body simulation to study the origin of possible correlations between the merging history and spin of cold dark matter haloes. In particular, we examine claims that remnants of major mergers tend to have higher-than-average spins, and find that the effect is driven largely by unrelaxed systems: equilibrium dark matter haloes show no significant correlation between spin and merging history. Out-of-equilibrium haloes have, on average, higher spin than relaxed systems, suggesting that the virialization process leads to a net decrease in the value of the spin parameter. We find that this decrease is due to the internal redistribution of mass and angular momentum that occurs during virialization. This process is especially efficient during major mergers, when high angular momentum material is pushed beyond the virial radius of the remnant. Because such redistribution likely affects the angular momentum of baryons and dark matter unevenly, our findings question the common practice of identifying the specific angular momentum content of a halo with that of its embedded luminous component. Further work is needed to elucidate the true relation between the angular momentum content of baryons and dark matter in galaxy systems assembled hierarchically.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".