Coarse-graining the distribution function of cold dark matter
Bibliographic record
Abstract
Many workers have found that the recollapse of a dark matter halo after decoupling has a self-similar dynamical phase. This behaviour is maintained strictly so long as the infall continues, but it appears to evolve smoothly into the virialized steady state and to transmit some of its properties intact. The density profiles established in this phase are all close to the isothermal inverse-square law, however, which is steeper than the predictions of some N-body simulations for the central regions of the halo, which are in turn steeper than the density profiles observed in the central regions of some galaxies, particularly dwarfs and low-surface-brightness galaxies. The outer regions of galaxies both as observed and as simulated have density profiles steeper than the self-similar profile. Nevertheless, there appears to be an intermediate region in most galaxies in which the inverse-square behaviour is a good description. The outer deviations can be explained plausibly in terms of the transition from a self-gravitating extended halo to a Keplerian flow on to a dominant central mass (the isothermal distribution cannot be complete), but the inner deviations are more problematic. Rather than attack this question directly, we use in this paper a novel coarse-graining technique combined with a shell code to establish both the distribution function associated with the self-similar density profile and the nature of the possible deviations in the central regions. In spherical symmetry we find that both in the case of purely radial orbits and in the case of orbits with non-zero angular momentum the self-similar density profile should flatten progressively near the centre of the system. The NFW limit of −1 seems possible. In a section aimed at demonstrating our technique for a spherically symmetric steady state, we argue that a Gaussian distribution function is the best approximation near the centre of the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".