A universal model for the evolution of tidally stripped systems
Bibliographic record
Abstract
ABSTRACT Accurate models of the structural evolution of dark matter subhaloes, as they orbit within larger systems, are fundamental to understanding the detailed distribution of dark matter at the present day. Numerical simulations of subhalo evolution support the idea that the mass-loss associated with tidal stripping is most naturally understood in energy space, with the particles that are the least bound being removed first. Starting from this premise, we recently proposed a zero-parameter ‘energy-truncation model’ for subhalo evolution. We tested this model with simulations of tidal stripping of satellites with initial NFW profiles, and showed that the energy-truncation model accurately predicts both the mass-loss and density profiles. In this work, we apply the model to a variety of Hernquist, Einasto, and King profiles. We show that it matches the simulation results quite closely in all cases, indicating that it may serve as a universal model to describe tidally stripped collision-less systems. A key prediction of the energy-truncation model is that the central density of dark matter subhaloes is conserved as they lose mass; this has important implications for dark matter annihilation calculations, and for other observational tests of dark matter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".