WHAT DO DARK MATTER HALO PROPERTIES TELL US ABOUT THEIR MASS ASSEMBLY HISTORIES?
Bibliographic record
Abstract
Individual dark matter halos in cosmological simulations vary widely in their detailed structural properties, properties such as concentration, shape, spin, and degree of internal relaxation. Recent non-parametric (principal component) analyses suggest that a few principal components explain a large fraction of the scatter in these structural properties. The main principal component is closely aligned with concentration, which in turn is known to be related to the mass accretion history (MAH) of the halo, as described by its merger tree. Here, we examine more generally the connection between the MAH and structural parameters. The space of mass accretion histories has principal components of its own. The strongest, accounting for almost 60% of the scatter between individual histories, can be interpreted as the age of the system. We give an analytic fit for this first component, which provides a rigorous way of defining the dynamical age of a halo. The second strongest component, representing acceleration or deceleration of growth at late times, accounts for 25% of the scatter. Relating structural parameters to formation history, we find that concentration correlates strongly with the early history of the halo, while shape and degree of relaxation or dynamical equilibrium correlate with the later history. We examine the inferences about formation history that can be drawn by splitting halos into sub-samples based on observable properties such as concentration and shape. Applications include the definition young and old samples of galaxy clusters in a quantitative way, or empirical tests of environmental processing rates in clusters.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".