Dynamical friction and feedback on galactic bars in the general fast–slow regime
Bibliographic record
Abstract
ABSTRACT Current theories of dynamical friction on galactic bars are based either on linear perturbation theory, which is valid only in the fast limit where the bar changes its pattern speed rapidly, or on adiabatic theory, which is applicable only in the slow limit where the bar’s pattern speed is near-constant. In this paper, we study dynamical friction on galactic bars spinning down at an arbitrary speed, seamlessly connecting the fast and slow limits. We treat the bar–halo interaction as a restricted N-body problem and solve the collisionless Boltzmann equation using the fast-angle-averaged Hamiltonian. The phase-space distribution and density wakes predicted by our averaged model are in excellent agreement with full 3D simulations. In the slow regime where resonant trapping occurs, we show that, in addition to the frictional torque, angular momentum is transferred directly due to the migration of the trapped phase-space: trapped orbits comoving with the resonance typically gain angular momentum, while untrapped orbits leaping over the trapped island lose angular momentum. Due to the negative gradient in the distribution function, gainers typically outnumber the losers, resulting in a net negative torque on the perturber. Part of this torque due to the untrapped orbits was already identified by Tremaine and Weinberg, who named the phenomenon dynamical feedback. Here, we derive the complete formula for dynamical feedback, accounting for both trapped and untrapped orbits. Using our revised formula, we show that dynamical feedback can account for up to 30 per cent of the total torque on the Milky Way’s bar.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".