Cosmological simulations of mixed ultralight dark matter
Bibliographic record
Abstract
The era of precision cosmology allows us to test the composition of the dark matter. Mixed ultralight or fuzzy dark matter (FDM) is a cosmological model with dark matter composed of a combination of particles of mass $m\leq 10^{-20}\;\mathrm{eV}$, with an astrophysical de Broglie wavelength, and particles with a negligible wavelength sharing the properties of cold dark matter (CDM). In this work, we simulate cosmological volumes with a dark matter wave function for the ultralight component coupled gravitationally to CDM particles. We investigate the impact of a mixture of CDM and FDM in various proportions $(0\%,\;1\%,\;10\%,\;50\%,\;100\%)$ and for ultralight particle masses ranging over five orders of magnitude $(2.5\times 10^{-25}\;\mathrm{eV}-2.5\times 10^{-21}\;\mathrm{eV})$. To track the evolution of density perturbations in the non-linear regime, we adapt the simulation code AxioNyx to solve the CDM dynamics coupled to a FDM wave function obeying the Schrödinger-Poisson equations. We obtain the non-linear power spectrum and study the impact of the wave effects on the growth of structure on different scales. We confirm that the steady-state solution of the Schrödinger-Poisson system holds at the center of halos in the presence of a CDM component when it composes $50\%$ or less of the dark matter but find no stable density core when the FDM accounts for $10\%$ or less of the dark matter. We implement a modified friends-of-friends halo finder and find good agreement between the observed halo abundance and the predictions from the adapted halo model axionHMCode.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".