USING GAPS IN<i>N</i>-BODY TIDAL STREAMS TO PROBE MISSING SATELLITES
Bibliographic record
Abstract
We use N -body simulations to model the tidal disruption of a star cluster in a Milky-Way-sized dark matter halo, which results in a narrow stream comparable to (but slightly wider than) Pal-5 or GD-1. The mean Galactic dark matter halo is modeled by a spherical Navarro–Frenk–White potential with subhalos predicted by the ΛCDM cosmological model. The distribution and mass function of the subhalos follow the results from the Aquarius simulation. We use a matched filter approach to look for "gaps" in tidal streams at 12 length scales from 0.1 kpc to 5 kpc, which appear as characteristic dips in the linear densities along the streams. We find that, in addition to the subhalos' perturbations, the epicyclic overdensities (EOs) due to the coherent epicyclic motions of particles in a stream also produce gap-like signals near the progenitor. We measure the gap spectra—the gap formation rates as functions of gap length—due to both subhalo perturbations and EOs, which have not been accounted for together by previous studies. Finally, we project the simulated streams onto the sky to investigate issues when interpreting gap spectra in observations. In particular, we find that gap spectra from low signal-to-noise observations can be biased by the orbital phase of the stream. This indicates that the study of stream gaps will benefit greatly from high-quality data from future missions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".