Magellanic satellites in ΛCDM cosmological hydrodynamical simulations of the Local Group
Bibliographic record
Abstract
ABSTRACT We use the APOSTLE ΛCDM cosmological hydrodynamical simulations of the Local Group to study the recent accretion of massive satellites into the halo of Milky Way (MW)-sized galaxies. These systems are selected to be close analogues to the Large Magellanic Cloud (LMC), the most massive satellite of the MW. The simulations allow us to address, in a cosmological context, the impact of the Clouds on the MW, including the contribution of Magellanic satellites to the MW satellite population, and the constraints placed on the Galactic potential by the motion of the LMC. We show that LMC-like satellites are twice more common around Local Group-like primaries than around isolated haloes of similar mass; these satellites come from large turnaround radii and are on highly eccentric orbits whose velocities at first pericentre are comparable with the primary’s escape velocity. This implies $V_{\rm esc}^{\rm MW} (50$ kpc) ∼ 365 km s−1, a strong constraint on Galactic potential models. LMC analogues contribute about two satellites with $M_*\gt 10^5\, \mathrm{ M}_\odot$, having thus only a mild impact on the luminous satellite population of their hosts. At first pericentre, LMC-associated satellites are close to the LMC in position and velocity, and are distributed along the LMC’s orbital plane. Their orbital angular momenta roughly align with the LMC’s, but, interestingly, they may appear to ‘counter-rotate’ the MW in some cases. These criteria refine earlier estimates of the LMC association of MW satellites: only the SMC, Hydrus1, Car3, Hor1, Tuc4, Ret2, and Phoenix2 are compatible with all criteria. Carina, Grus2, Hor2, and Fornax are less probable associates given their large LMC relative velocity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".