CFHTLenS: The Canada-France-Hawaii telescope lensing survey - Imaging data and catalogue products
Bibliographic record
Abstract
We present data products from the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS). CFHTLenS is based on the Wide component of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). It encompasses 154 deg(2) of deep, optical, high-quality, sub-arcsecond imaging data in the five optical filters u*g(')r(')i(')z('). The scientific aims of the CFHTLenS team are weak gravitational lensing studies supported by photometric redshift estimates for the galaxies. This paper presents our data processing of the complete CFHTLenS data set. We were able to obtain a data set with very good image quality and high-quality astrometric and photometric calibration. Our external astrometric accuracy is between 60 and 70 mas with respect to Sloan Digital Sky Survey (SDSS) data, and the internal alignment in all filters is around 30 mas. Our average photometric calibration shows a dispersion of the order of 0.01-0.03 mag for g(')r(')i(')z(') and about 0.04 mag for u* with respect to SDSS sources down to i(SDSS) < 21. We demonstrate in accompanying papers that our data meet necessary requirements to fully exploit the survey for weak gravitational lensing analyses in connection with photometric redshift studies. In the spirit of the CFHTLS, all our data products are released to the astronomical community via the Canadian Astronomy Data Centre athttp://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/CFHTLens/query.html. We give a description and how-to manuals of the public products which include image pixel data, source catalogues with photometric redshift estimates and all relevant quantities to perform weak lensing studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".