Soliton self-gravity and core-halo relation in fuzzy dark matter halos
Bibliographic record
Abstract
Abstract Fuzzy dark matter (FDM) is an attractive dark matter candidate composed of ultralight particles. In this paper, toward a clear understanding of the core-halo relation in the FDM halos, we consider a simple model of the soliton-halo system, in which the self-gravitating soliton core is formed in the presence of Navarro-Frenk-White (NFW) halo potential as an external field. Solving numerically the Schrödinger-Poisson equation, the self-gravitating soliton is obtained as a ground-state solution, which is characterized by the two key parameters, i.e., size of soliton core and its strength of self-gravity relative to those of the NFW halo. Using our soliton-halo model, we investigate the properties of soliton cores found in cosmological simulation, and the key parameters characterizing these solitons are reconstructed in a self-consistent manner. Results suggest that (1) the soliton core properties depend critically on both the self-gravity of the soliton and the external potential of the host halo, and (2) the scatter observed in the core-halo relation cannot be explained solely by the one in the halo's concentration-mass relation, as previously suggested, but also significantly influenced by intrinsic features of the soliton core, potentially arising from local dynamics at the halo center. We also demonstrate that the FDM mass can be reconstructed from the simulation data characterizing the halo density profile, providing a basis for applying the model to observational studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".