Phase-space structure in the local dark matter distribution and its signature in direct detection experiments
Bibliographic record
Abstract
We study predictions for dark matter (DM) phase-space structure near the Sun based on high-resolution simulations of six galaxy haloes taken from the Aquarius project. The local DM density distribution is predicted to be remarkably smooth; the density at the Sun differs from the mean over a best-fitting ellipsoidal equidensity contour by less than 15 per cent at the 99.9 per cent confidence level. The local velocity distribution is also very smooth, but it differs systematically from a (multivariate) Gaussian distribution. This is not due to the presence of individual clumps or streams, but to broad features in the velocity modulus and energy distributions that are stable in both space and time and reflect the detailed assembly history of each halo. These features have a significant impact on the signals predicted for weakly interacting massive particle and axion searches. For example, weakly interacting massive particles recoil rates can deviate by ∼10 per cent from those expected from the best-fitting multivariate Gaussian models. The axion spectra in our simulations typically peak at lower frequencies than in the case of multivariate Gaussian velocity distributions. Also in this case, the spectra show significant imprints of the formation of the halo. This implies that once direct DM detection has become routine, features in the detector signal will allow us to study the DM assembly history of the Milky Way. A new field, ‘DM astronomy’, will then emerge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".