Preprint typeset using L ATEX style emulateapj v. 11/26/04 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 deg 2 (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 σ8, the amplitude of the matter power spectrum. At a fiducial matter density Ωm = 0.3 we find σ8 = 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 w0 < −0.8 at 68 % confidence. Subject headings: cosmology: observations − dark matter − gravitational lensing 1.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.485 | 0.332 |
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".