Wave interference in self-interacting fuzzy dark matter
Bibliographic record
Abstract
In the fuzzy dark matter (FDM) scenario, dark matter is composed of an ultralight scalar field with coherence length and wave interference on astrophysical scales. Scalar fields generically have quartic self-interactions that modify their dispersion relation and the associated evolution of density perturbations. We perform the first dedicated analysis of the role of wave interference on this evolution due to self-interactions in FDM and vice versa, developing a perturbative treatment applicable at early times and then comparing against a suite of fully nonlinear benchmark simulations, varying the dark matter density, interaction strength, and fiducial momentum scale. We explicitly simulate the limit where this momentum scale is relatively high compared with the scale of the simulation volume, applicable to cases where the dark matter is initially ``warm'' due to causal constraints on a postinflationary production or in virialized halos and other ``thermalized'' cases with initially cold production. We find that in such scenarios, density perturbations are unable to grow on the expected self-interaction time scale because of interference effects, instead saturating on the much shorter de Broglie crossing time, with a dependence on the sign of the interaction. Finally, we comment on the implications of our results for astrophysical systems such as high-density ultrafaint dwarf galaxies where wave interference plays an important role.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".