Dark Energy Survey Year 3 results: Cosmology with moments of weak lensing mass maps
Bibliographic record
Abstract
We present a cosmological analysis using the second and third moments of the weak lensing mass (convergence) maps from the first three years of data (Y3) data of the Dark Energy Survey. The survey spans an effective area of 4139 square degrees and uses the images of over 100 million galaxies to reconstruct the convergence field. The second moment of the convergence as a function of smoothing scale contains information similar to standard shear 2-point statistics. The third moment, or the skewness, contains additional non-Gaussian information. The data is analyzed in the context of the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model, varying five cosmological parameters and 19 nuisance parameters modeling astrophysical and measurement systematics. Our modeling of the observables is completely analytical, and has been tested with simulations in our previous methodology study. We obtain a 1.7% measurement of the amplitude of fluctuations parameter ${S}_{8}\ensuremath{\equiv}{\ensuremath{\sigma}}_{8}({\mathrm{\ensuremath{\Omega}}}_{m}/0.3{)}^{0.5}=0.784\ifmmode\pm\else\textpm\fi{}0.013$. The measurements are shown to be internally consistent across redshift bins, angular scales, and between second and third moments. In particular, the measured third moment is consistent with the expectation of gravitational clustering under the $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ model. The addition of the third moment improves the constraints on ${S}_{8}$ and ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}$ by $\ensuremath{\sim}15%$ and $\ensuremath{\sim}25%$ compared to an analysis that only uses second moments. We compare our results with Planck constraints from the cosmic microwave background, finding a $2.2--2.8\ensuremath{\sigma}$ tension in the full parameter space, depending on the combination of moments considered. The third moment, independently, is in $2.8\ensuremath{\sigma}$ tension with Planck, and thus provides a cross-check on the analyses of 2-point correlations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".