Measuring cosmic shear with the ring statistics
Bibliographic record
Abstract
Context. Commonly used methods of decomposing E- and B-modes in cosmic shear, namely the aperture mass dispersion and the E/B-mode shear correlation function, suffer from incomplete knowledge of the two-point correlation function (2PCF) on very small and/or very large scales. The ring statistics, the most recently developed cosmic shear measure, improves on this issue and is able to decompose E- and B-modes using a 2PCF measured on a finite interval.Aims. First, we improve on the ring statistics' filter function over the signal-to-noise ratio (S/N). Second, we examine the ability of the ring statistics to constrain cosmology and compare the results to cosmological constraints obtained with the aperture mass dispersion. Third, we use the ring statistics to measure a cosmic shear signal from CFHTLS (Canada-France-Hawaii Telescope Legacy Survey) data.Methods. We consider a scale-dependent filter function for the ring statistics, which improves its S/N. To examine the information content of the ring statistics, we employed ray-tracing simulations and developed an expression of the ring statistics' covariance in terms of a 2PCF covariance. We performed a likelihood analysis with simulated data for the ring statistics in the Ωm-σ8 parameter space and compared the information content of ring statistics and aperture mass dispersion. Regarding our third aim, we used the 2PCF of the latest CFHTLS analysis to calculate the ring statistics and its error bars.Results. Although the scale-dependent filter function improves the S/N of the ring statistics, the S/N of the aperture mass dispersion is higher. In addition, we show that filter functions exist that decompose E- and B-modes using a finite range of 2PCFs (EB-statistics) and have higher S/N than the ring statistics. However, we find that data points of the latter are significantly less correlated than data points of the aperture mass dispersion and the EB-statistics. As a consequence the ring statistics is an ideal tool for identifying remaining systematics accurately as a function of angular scale. We use the ring statistics to measure a E- and B-mode shear signal from CFHTLS data.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".