DYNAMICAL PROPERTIES OF COLLISIONLESS STAR STREAMS
Bibliographic record
Abstract
A sufficiently extended satellite in the tidal field of a host galaxy loses mass to create nearly symmetric leading and trailing tidal streams. We study the case in which tidal heating drives mass loss from a low mass satellite. The stream effectively has two dynamical components, a common angular momentum core superposed with episodic pulses with a broader angular momentum distribution. The pulses appear as spurs on the stream, oscillating above and below the stream centerline, stretching and blurring in configuration space as they move away from the cluster. Low orbital eccentricity streams are smoother and have less differential motion than high eccentricity streams. The tail of a high eccentricity stream can develop a fan of particles that wraps around at apocenter in a shell feature. We show that scaling the essentially stationary action-angle variables with the cube root of the satellite mass allows a low mass satellite stream to accurately predict the features in the stream from a satellite a thousand times more massive. As a practical astrophysical application, we demonstrate that narrow gaps in a moderate eccentricity stream, such as GD-1, blur out to 50% contrast over approximately six radial periods. A high eccentricity stream, such as Pal 5, will blur small gaps in only two radial orbits as can be understood from the much larger dispersion of angular momentum in the stream.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".