Predicting Substructure in CDM Haloes
Bibliographic record
Abstract
Abstract. Observations of multiple-image gravitational lens systems suggest that the projected mass distributions of galaxy haloes may contain substantial inhomogeneities. The fraction of the halo mass in dense substructure is still highly uncertain, but could be as large as a few percent. While halo substructure is seen in numerical simulations of CDM haloes, little of this substructure survives in the innermost regions of haloes, and thus the observational claims for substructure at small projected radii are slightly surprising. There is evidence, however, that even the highest-resolution simulations published to-date are still limited by numerical effects that heat and disrupt substructure artificially in high-density regions. By comparing numerical and semi-analytic (SA) models of halo substructure, we show that current simulations probably underestimate the mass fraction in substructure at small projected radii, by a factor of at least 2–3. We discuss the prospects for using lensing observations as a fundamental test of the nature of dark matter. 1. Introduction: The
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".