Axion dark matter, solitons and the cusp–core problem
Bibliographic record
Abstract
Self-gravitating bosonic fields can support stable and localized (solitonic) field configurations. Such solitons should be ubiquitous in models of axion dark matter, with their characteristic mass and size depending on some inverse power of the axion mass, ma. Using a scaling symmetry and the uncertainty principle, the soliton core size can be related to the central density and axion mass in a universal way. Solitons have a constant central density due to pressure support, unlike the cuspy profile of cold dark matter (CDM). Consequently, solitons composed of ultralight axions (ULAs) may resolve the ‘cusp–core’ problem of CDM. In dark matter (DM) haloes, thermodynamics will lead to a CDM-like Navarro–Frenk–White (NFW) profile at large radii, with a central soliton core at small radii. Using Monte Carlo techniques to explore the possible density profiles of this form, a fit to stellar kinematical data of dwarf spheroidal galaxies is performed. The data favour cores, and show no preference concerning the NFW part of the halo. In order for ULAs to resolve the cusp–core problem (without recourse to baryon feedback, or other astrophysical effects) the axion mass must satisfy ma < 1.1 × 10−22 eV at 95 per cent C.L. However, ULAs with ma ≲ 1 × 10−22 eV are in some tension with cosmological structure formation. An axion solution to the cusp–core problem thus makes novel predictions for future measurements of the epoch of reionization. On the other hand, improved measurements of structure formation could soon impose a Catch 22 on axion/scalar field DM, similar to the case of warm DM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".