Detection of correlated galaxy ellipticities on CFHT data: first evidence for gravitational lensing by large-scale structures
Bibliographic record
Abstract
We report the detection of a significant (5.5 sigma) excess of correlations between galaxy ellipticities at scales ranging from 0.5 to 3.5 arc-minutes. This detection of a gravitational lensing signal by large-scale structure was made using a composite high quality imaging survey of 6300 arcmin^2 obtained at the Canada France Hawaii Telescope (CFHT) with the UH8K and CFH12K panoramic CCD cameras. The amplitude of the excess correlation is 2.2\pm 0.2 % at 1 arcmin scale, in agreement with theoretical predictions of the lensing effect induced by large-scale structure.We provide a quantitative analysis of systematics which could contribute to the signal and show that the net effect is small and can be corrected for. We show that the measured ellipticity correlations behave as expected for a gravitational shear signal. The relatively small size of our survey precludes tight constraints on cosmological models. However the data are in favor of cluster normalized cosmological models, and marginally reject Cold Dark Matter models with (Omega=0.3, sigma_8<0.6) or (Omega=1, sigma_8=1). The detection of cosmic shear demonstrates the technical feasibility of using weak lensing surveys to measure dark matter clustering and the potential for cosmological parameter measurements, in particular with upcoming wide field CCD cameras.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".