High-resolution simulations of dark matter subhalo disruption in a Milky-Way-like tidal field
Bibliographic record
Abstract
ABSTRACT We compare the results of high-resolution simulations of individual dark matter subhaloes evolving in external tidal fields with and without baryonic bulge and disc components, where the average dark matter particle mass is three orders of magnitude smaller than cosmological zoom-in simulations of galaxy formation. The Via Lactea II simulation is used to setup our initial conditions and provides a basis for our simulations of subhaloes in a dark-matter-only tidal field, while an observationally motivated model for the Milky-Way is used for the tidal field that is comprised of a dark matter halo, a stellar disc, and a stellar bulge. Our simulations indicate that including stellar components in the tidal field results in the number of subhaloes in Milky-Way-like galaxies being only $65{{\ \rm per\ cent}}$ of what is predicted by Λ cold dark matter (ΛCDM). For subhaloes with small pericentres (rp ≲ 25 kpc), the subhalo abundance is reduced further to $40{{\ \rm per\ cent}}$, with the surviving subhaloes being less dense and having a tangentially anisotropic orbital distribution. Conversely, subhaloes with larger pericentres are minimally affected by the inclusion of a stellar component in the tidal field, with the total number of outer subhaloes $\approx 75{{\ \rm per\ cent}}$ of the ΛCDM prediction. The densities of outer subhaloes are comparable to predictions from ΛCDM, with the subhaloes having an isotropic distribution of orbits. These ratios are higher than those found in previous studies that include the effects baryonic matter, which are affected by spurious disruption caused by low resolution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".