Background sky obscuration by cluster galaxies as a source of systematic error for weak lensing
Bibliographic record
Abstract
Lensing magnification and stacked shear measurements of galaxy clusters rely on measuring the density of background galaxies behind the clusters. The most common ways of measuring this quantity ignore the fact that some fraction of the sky is obscured by the cluster galaxies themselves, reducing the area in which background galaxies can be observed. We discuss the size of this effect in the Sloan Digital Sky Survey (SDSS) and the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS), finding a minimum 1 per cent effect at 0.1 h−1 Mpc from the centres of clusters in SDSS; the effect is an order of magnitude higher in CFHTLenS. The resulting biases on cluster mass and concentration measurements are of the same order as the size of the obscuration effect, which is below the statistical errors for cluster lensing in SDSS but likely exceeds them for CFHTLenS. We also forecast the impact of this systematic error on cluster mass and magnification measurements in several upcoming surveys, and find that it typically exceeds the statistical errors. We conclude that future surveys must account for this effect in stacked lensing and magnification measurements in order to avoid being dominated by systematic error.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".