Simulations of satellite tidal debris in the Milky Way halo
Bibliographic record
Abstract
Aims. We study the distribution of the stellar and dark matter debris of the Milky Way satellites. Methods. For the first time we address the question of the tidal disruption of satellites in simulations by utilising simultaneously (a) a realistic set of orbits extracted from cosmological simulations; (b) a three-component host galaxy with live halo, disc, and bulge components; and (c) satellites from hydrodynamical simulations. We analyse the statistical properties of the satellite debris of all massive galaxies reaching the inner Milky Way on a timescale of 2 Gyr. Results. Up to 80% of the dark matter is stripped from the satellites, while this happens for up to 30% of their stars. The stellar debris ends mostly in the inner Milky Way halo, whereas the dark matter debris shows a flat mass distribution over the full main halo. The dark matter debris follows a density profile with inner power law index αDM = −0.66 and outer index βDM = 2.94, while for stars α* = −0.44 and β* = 6.17. In the inner 25 kpc the distribution of the stellar debris is flatter than that of the dark matter debris, and the orientations of their short axes differ significantly. Changing the orientation of the stellar disc by 90° has a minor impact on the distribution of the satellite debris. Conclusions. Our results indicate that dark matter is more easily stripped than stars from the Milky Way satellites. The structure of the debris is dominated by the satellite orbital properties. The radial profiles, the flattening, and the orientation of the stellar and dark matter debris are significantly different, which prevents the prediction of the dark matter distribution from the observed stellar component.
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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 | 0.003 |
| 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 | 0.001 |
| 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".