Dynamical self-friction: how mass loss slows you down
Bibliographic record
Abstract
ABSTRACT We investigate dynamical self-friction, the process by which material that is stripped from a subhalo torques its remaining bound remnant, which causes it to lose orbital angular momentum. By running idealized simulations of a subhalo orbiting within an analytical host halo potential, we isolate the effect of self-friction from traditional dynamical friction due to the host halo. While at some points in a subhalo’s orbit the torque of the stripped material can boost the orbital angular momentum of the remnant, the net effect over the long term is orbital decay regardless of the initial orbital parameters or subhalo mass. In order to quantify the strength of self-friction, we run a suite of simulations spanning typical host-to-subhalo mass ratios and orbital parameters. We find that the time-scale for self-friction, defined as the exponential decay time of the subhalo’s orbital angular momentum, scales with mass ratio and orbital circularity similar to standard dynamical friction. The decay time due to self-friction is roughly an order of magnitude longer, suggesting that self-friction only contributes at the 10 per cent level. However, along more radial orbits, self-friction can occasionally dominate over dynamical friction close to pericentric passage, where mass stripping is intense. This is also the epoch at which the self-friction torque undergoes large and rapid changes in both magnitude and direction, indicating that self-friction is an important process to consider when modelling pericentric passages of subhaloes and their associated satellite galaxies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".