Cosmic shear analysis with CFHTLS deep data
Bibliographic record
Abstract
We present the first cosmic shear measurements obtained from the T0001 release of the Canada-France-Hawaii Telescope Legacy Survey. The data set covers three uncorrelated patches (D1, D3 and D4) of one square degree each, observed in u*, g', r', i' and z' bands, to a depth of . The deep, multi-colour observations in these fields allow for several data-quality controls. The lensing signal is detected in both r' and i' bands and shows similar amplitude and slope in both filters. B-modes are found to be statistically zero at all scales. Using multi-colour information, we derived a photometric redshift for each galaxy and use this to separate the background source sample into low-z and high-z subsamples. A stronger shear signal is detected from the high-z subsample than from the low-z subsample, as expected from weak lensing tomography. While further work is needed to model the effects of errors in the photometric redshifts, this result suggests that it will be possible to obtain constraints on the growth of dark matter fluctuations with lensing wide field surveys. The combined Deep and Wide surveys give assuming the Peacock & Dodds non-linear scheme (P&D), and for the halo model and . We assumed a Cold Dark Matter model with flat geometry and have marginalized over the systematics, the Hubble constant and redshift uncertainties. Using data from the Deep survey, the upper bound for w0, the constant equation of state parameter is .
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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 | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".