Detection of Dark Matter Skewness in the VIRMOS‐DESCART Survey: Implications for Ω<sub>0</sub>
Bibliographic record
Abstract
Weak gravitational lensing provides a direct statistical measure of dark matter distribution. The variance is easiest to measure, which constrains the degenerate product σ 8 Ω . The degeneracy is broken by measuring the skewness arising from the fact that densities must remain positive, which is not possible when the initially symmetric perturbations become nonlinear. Skewness measures the nonlinear mass scale, which in combination with the variance measures Ω 0 directly. We present the first detection of dark matter skewness from the VIRMOS-DESCART survey. We have measured the full three-point function and its projections onto windowed skewness. We separate the lensing mode and the B -mode. The lensing skewness is detected for a compensated Gaussian on scales of 5 37 to be 3 = (1.06 ± 0.06) × 10 -6 . The B -modes are consistent with zero at this scale. The variance for the same window function is 2 = (5.32 ± 0.62 ± 0.98) × 10 -5 , resulting in S 3 = 375 . Comparing with N -body simulations, we find Ω 0 < 0.5 at 90% confidence. The Canada-France-Hawaii Telescope legacy survey and newer simulations should be able to improve significantly on the constraint.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".