Properties of Local Group galaxies in hydrodynamical simulations of sterile neutrino dark matter cosmologies
Bibliographic record
Abstract
Abstract We study galaxy formation in sterile neutrino dark matter models that differ significantly from both cold and from ‘warm thermal relic’ models. We use the eagle code to carry out hydrodynamic simulations of the evolution of pairs of galaxies chosen to resemble the Local Group, as part of the APOSTLE simulations project. We compare cold dark matter (CDM) with two sterile neutrino models with 7 keV mass: one, the warmest among all models of this mass (LA120) and the other, a relatively cold case (LA10). We show that the lower concentration of sterile neutrino subhaloes compared to their CDM counterparts makes the inferred inner dark matter content of galaxies like Fornax (or Magellanic Clouds) less of an outlier in the sterile neutrino cosmologies. In terms of the galaxy number counts, the LA10 simulations are indistinguishable from CDM when one takes into account halo-to-halo (or ‘simulation-to-simulation’) scatter. In order for the LA120 model to match the number of Local Group dwarf galaxies, a higher fraction of low-mass haloes is required to form galaxies than is predicted by the eagle simulations. As the census of the Local Group galaxies nears completion, this population may provide a strong discriminant between cold and warm dark matter models.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".