No <i>Catch-22</i> for fuzzy dark matter: testing substructure counts and core sizes via high-resolution cosmological simulations
Bibliographic record
Abstract
ABSTRACT Fuzzy dark matter (FDM) has recently emerged as an interesting alternative model to the standard cold dark matter (CDM). In this model, dark matter consists of very light bosonic particles with wave-like behaviour on galactic scales. Using the N-body code ax-gadget, we perform cosmological simulations of FDM that fully model the dynamical effects of the quantum potential throughout cosmic evolution. Through the combined analysis of FDM volume and high-resolution zoom-in simulations of different FDM particle masses ($m_{\chi }$$\sim$$10^{-23}\!-\!10^{-21}$ eV c−2), we study how FDM impacts the abundance of substructure and the inner density profiles of dark matter haloes. For the first time, using our FDM volume simulations, we provide a fitting formula for the FDM-to-CDM subhalo abundance ratio as a function of the FDM mass. More importantly, our simulations clearly demonstrate that there exists an extended FDM particle mass interval able to reproduce the observed substructure counts and, at the same time, create substantial cores ($r_{c} \sim 1$ kpc) in the density profile of dwarf galaxies ($\approx 10^{9}\!-\!10^{10}$ M$_{\odot }$), which stands in stark contrast with CDM predictions even with baryonic effects taken into account. The dark matter distribution in the faintest galaxies offers then a clear way to discriminate between FDM and CDM.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".