Fuzzy Aquarius: evolution of a Milky-way like system in the Fuzzy Dark Matter scenario
Bibliographic record
Abstract
ABSTRACT We present the first high-resolution zoom-in simulation of a Milky-way-like halo extracted from the Aquarius Project in the Fuzzy Dark Matter (FDM) framework. We use the N-body code AX-GADGET, based on a particle-oriented solution of the Schrödinger–Poisson equations, able to detail the complexity of structure formation while keeping track of the quantum effects in FDM. The halo shows a cored density profile, with a core size of several kpc for an FDM mass of mχ = 2.5h × 10−22 eV/c2. A flattening is observed also in the velocity profile, representing a distinct feature of FDM dynamics. We provide a quantitative analysis of the impact of fuzziness on subhaloes in terms of abundance, mass, distance, and velocity distribution functions, and their evolution with redshift. Very interestingly, we show that all collapsed structures, despite showing a flat density profile at z = 0, do not reach the solitonic ground state at the time of formation: on the contrary, they asymptotically converge to it on a time-scale that depends on their mass and formation history. This implies that current limits on FDM mass – obtained by applying simple scaling relations to observed galaxies – should be taken with extreme care, since single objects can significantly deviate from the expected asymptotic behaviour during their evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".