Subhalo destruction in the Apostle and Auriga simulations
Bibliographic record
Abstract
ABSTRACT N-body simulations make unambiguous predictions for the abundance of substructures within dark matter haloes. However, the inclusion of baryons in the simulations changes the picture because processes associated with the presence of a large galaxy in the halo can destroy subhaloes and substantially alter the mass function and velocity distribution of subhaloes. We compare the effect of galaxy formation on subhalo populations in two state-of-the-art sets of hydrodynamical Λcold dark matter (ΛCDM) simulations of Milky Way mass haloes, Apostle and Auriga. We introduce a new method for tracking the orbits of subhaloes between simulation snapshots that gives accurate results down to a few kiloparsecs from the centre of the halo. Relative to a dark matter-only simulation, the abundance of subhaloes in Apostle is reduced by 50 per cent near the centre and by 10 per cent within r200. In Auriga, the corresponding numbers are 80 per cent and 40 per cent. The velocity distributions of subhaloes are also affected by the presence of the galaxy, much more so in Auriga than in Apostle. The differences on subhalo properties in the two simulations can be traced back to the mass of the central galaxies, which in Auriga are typically twice as massive as those in Apostle. We show that some of the results from previous studies are inaccurate due to systematic errors in the modelling of subhalo orbits near the centre of haloes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".