Do assumptions about the central density of subhaloes affect dark matter annihilation and lensing calculations?
Bibliographic record
Abstract
Abstract Subhalo models play a critical role in dark matter annihilation predictions and galaxy-galaxy lensing studies; however the internal structure of subhaloes remains highly uncertain. In particular, a growing body of evidence suggests that the central density of cuspy dark matter subhaloes is conserved in minor mergers, whereas empirical models of subhalo evolution — calibrated using limited-resolution simulations — often assume a drop in the central density. To assess the impact of these assumptions, we systematically explore how a wide range of initial mass profiles and tidal evolution prescriptions influence annihilation and lensing calculations, including the physically motivated Energy Truncation model, which explicitly preserves the central density of subhaloes. We find that annihilation calculations are very sensitive to the assumed inner density profile, and different models can produce more than an order of magnitude difference in the annihilation rate of individual subhaloes, and a factor of ∼5 in the total annihilation rate expected in the Milky Way. Since the innermost parts of haloes will always be difficult to resolve in simulations, we conclude that developing a theoretical understanding of subhalo evolution is crucial to be able to make accurate predictions of the dark matter annihilation signal. On the other hand, while the shear and convergence profiles used in galaxy-galaxy lensing are sensitive to the initial profile assumed (e.g., NFW versus Einasto), they are otherwise well-approximated by a simple stripping model in which the original profile is sharply truncated at a tidal radius.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".