Tidal stripping in the adiabatic limit
Bibliographic record
Abstract
ABSTRACT We present a model for the remnants of haloes that have gone through an adiabatic tidal stripping process. We show that this model exactly reproduces the remnant of an NFW halo that is exposed to a slowly increasing isotropic tidal field and approximately for an anisotropic tidal field. The model can be used to predict the asymptotic mass loss limit for orbiting subhaloes, solely as a function of the initial structure of the subhalo and the value of the tidal field at pericentre. Predictions can easily be made for differently concentrated host-haloes with and without baryonic components, which differ most notably in their relation between pericentre radius and tidal field. The model correctly predicts several empirically measured relations such as the ‘tidal track’ and the ‘orbital frequency relation’ that was reported by Errani & Navarro (2021) for the case of an isothermal sphere. Further, we propose applications of the ‘structure–tide’ degeneracy, which implies that increasing the concentration of a subhalo has exactly the same impact on tidal stripping as reducing the amplitude of the tidal field. Beyond this, we find that simple relations hold for the bound mass, truncation radius, WIMP annihilation luminosity, and tidal ratio of tidally stripped NFW haloes in relation to quantities measured at the radius of maximum circular velocity. Finally, we note that NFW haloes cannot be completely disrupted when exposed adiabatically to tidal fields of arbitrary magnitudes. We provide an open-source implementation of our model and suggest that it can be used to improve predictions of dark matter annihilation.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".