First cosmic shear results from the Canada-France-Hawaii Telescope Wide Synoptic Legacy Survey
Bibliographic record
Abstract
We present the first measurements of the weak gravitational lensing signal induced by the large scale mass distribution from data obtained as part of the ongoing Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). The data used in this analysis are from the Wide Synoptic Survey, which aims to image ~170 square degree in five filters. We have analysed ~22 deg2 (31 pointings) of i' data spread over two of the three survey fields. These data are of excellent quality and the results bode well for the remainder of the survey: we do not detect a significant `B'-mode, suggesting that residual systematics are negligible at the current level of accuracy. Assuming a Cold Dark Matter model and marginalising over the Hubble parameter h=[0.6,0.8], the source redshift distribution and systematics, we constrain sigma_8, the amplitude of the matter power spectrum. At a fiducial matter density Omega_m=0.3 we find sigma_8=0.85+-0.06. This estimate is in excellent agreement with previous studies. Combination of our results with those from the Deep component of the CFHTLS enables us to place a constraint on a constant equation of state for the dark energy, based on cosmic shear data alone. We find that w_0<-0.8 at 68% confidence.
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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 | 0.003 |
| 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) | 0.002 | 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".