Bibliographic record
Abstract
We develop a method for performing a weak lensing analysis using only measurements of galaxy position angles. By analysing the statistical properties of the galaxy orientations given a known intrinsic ellipticity distribution, we show that it is possible to obtain estimates of the shear by minimizing a χ2 statistic. The method is demonstrated using simulations where the components of the intrinsic ellipticity are taken to be Gaussian distributed. Uncertainties in the position angle measurements introduce a bias into the shear estimates which can be reduced to negligible levels by introducing a correction term into the formalism. We generalize our approach by developing an algorithm to obtain direct shear estimators given any azimuthally symmetric intrinsic ellipticity distribution. We introduce a method of measuring the position angles of the galaxies from noisy pixelized images, and propose a method to correct for biases which arise due to pixelization and correlations between measurement errors and galaxy ellipticities. We also develop a method to constrain the sample of galaxies used to obtain an estimate of the intrinsic ellipticity distribution such that fractional biases in the resulting shear estimates are below a given threshold value. We demonstrate the angle-only method by applying it to simulations where the ellipticities are taken to follow a lognormal distribution. We compare the performance of the position-angle-only method with the standard method based on full ellipticity measurements by reconstructing lensing convergence maps from both numerical simulations and from the Canada–France–Hawaii Lensing Survey data. We find that the difference between the convergence maps reconstructed using the two methods is consistent with noise.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".