Dark matter annihilation radiation in hydrodynamic simulations of Milky Way haloes
Bibliographic record
Abstract
We obtain predictions for the properties of cold dark matter annihilation radiation using high-resolution hydrodynamic zoom-in cosmological simulations of Milky Way-like galaxies (apostle project) carried out as part of the ‘Evolution and Assembly of GaLaxies and their Environments’ (eagle) programme. Galactic haloes in the simulation have significantly different properties from those assumed in the ‘standard halo model’ often used in dark matter detection studies. The formation of the galaxy causes a contraction of the dark matter halo, whose density profile develops a steeper slope than the Navarro–Frenk–White (NFW) profile between r ≈ 1.5 kpc and r ≈ 10 kpc. At smaller radii, r ≲ 1.5 kpc, the haloes develop a flatter than NFW slope. This unexpected feature may be specific to our particular choice of subgrid physics model but nevertheless the dark matter density profiles agree within 30 per cent as the mass resolution is increased by a factor 150. The inner regions of the haloes are almost perfectly spherical (axis ratios b/a > 0.97 within r = 1 kpc) and there is no offset larger than 45 pc between the centre of the stellar distribution and the centre of the dark halo. The morphology of the predicted dark matter annihilation radiation signal is in broad agreement with γ-ray observations at large Galactic latitudes (b ≳ 3°). At smaller angles, the inferred signal in one of our four galaxies is similar to that which is observed but it is significantly weaker in the other three.
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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 | 0.003 |
| 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 | 0.000 |
| 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".