Mass-loss in tidally stripped systems: the energy-based truncation method
Bibliographic record
Abstract
ABSTRACT The ability to accurately predict the evolution of tidally stripped haloes is important for understanding galaxy formation and testing the properties of dark matter. Most studies of substructure evolution make predictions based on empirical models of tidal mass-loss that are calibrated using numerical simulations. This approach can be accurate in the cases considered, but lacks generality and does not provide a physical understanding of the processes involved. Recently, we demonstrated that truncating NFW distribution functions sharply in energy results in density profiles that resemble those of tidally stripped systems, offering a path to constructing physically motivated models of tidal mass-loss. In this work, we review calculations of mass-loss based on energy truncation alone, and then consider what secondary effects may modulate mass-loss beyond this. We find that a combination of dependence on additional orbital parameters and variations in individual particle energies over an orbit results in a less abrupt truncation in energy space as a subhalo loses mass. Combining the energy truncation approach with a simple prediction for the mass-loss rate, we construct a full model of mass-loss that can accurately predict the evolution of a subhalo in terms of a single parameter ηeff. This parameter can be fully determined from the initial orbital and halo properties, and does not require calibration with numerical simulations.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".