Detection of dark matter Skewness in the VIRMOS-DESCART survey: Implications for \\Omega_0
Bibliographic record
Abstract
Weak gravitational lensing provides a direct statistical measure of the dark matter distribution. The variance is easiest to measure, which constrains the degenerate product \\sigma_8\\Omega^0.6. 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 non-linear. Skewness measures the non-linear mass scale, which in combination with the variance measures \\Omega directly. We present the first detection of dark matter skewness from the Virmos-Decart 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 arc minutes to be \\kappa^3=1.06+/- 0.06x10^-6. The B-modes are consistent with zero at this scale. The variance for the same window function is \\kappa^2= 5.32+/-0.62+/- 0.98x10^-5, resulting in S_3=375^{+342}_{-124}. Comparing to N-body simulations, we find \\Omega_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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".