Hunting for the Dark Matter Wake Induced by the Large Magellanic Cloud
Bibliographic record
Abstract
Abstract Satellite galaxies are predicted to generate gravitational density wakes as they orbit within the dark matter (DM) halos of their hosts, causing their orbits to decay over time. The recent infall of the Milky Way’s (MW) most massive satellite galaxy, the Large Magellanic Cloud (LMC), affords us the unique opportunity to study this process in action. In this work, we present high-resolution (m dm = 4 × 104 M ⊙) N-body simulations of the MW–LMC interaction over the past 2 Gyr. We quantify the impact of the LMC’s passage on the density and kinematics of the MW’s DM halo and the observability of these structures in the MW’s stellar halo. The LMC is found to generate a pronounced wake, which we decompose in Transient and Collective responses, in both the DM and stellar halos. The wake leads to overdensities and distinct kinematic patterns that should be observable with ongoing and future surveys. Specifically, the Collective response will result in redshifted radial velocities of stars in the north and blueshifts in the south, at distances >45 kpc. The Transient response traces the orbital path of the LMC through the halo (50–200 kpc), resulting in a stellar overdensity with a distinct, tangential kinematic pattern that persists to the present day. The detection of the MW’s halo response will constrain the infall mass of the LMC, its orbital trajectory, and the mass of the MW, and it may inform us about the nature of the DM particle itself.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".