WEAK LENSING MEASUREMENT OF GALAXY CLUSTERS IN THE CFHTLS-WIDE SURVEY
Bibliographic record
Abstract
We present the first weak gravitational lensing analysis of the completed Canada–France–Hawaii Telescope Legacy Survey (CFHTLS). We study the 64 deg 2 W1 field, the largest of the CFHTLS-Wide survey fields, and present the largest contiguous weak lensing convergence "mass map" yet made. 2.66 million galaxy shapes are measured, using the Kaiser Squires and Broadhurst Method (KSB) pipeline verified against high-resolution Hubble Space Telescope imaging that covers part of the CFHTLS. Our i '-band measurements are also consistent with an analysis of independent r '-band imaging. The reconstructed lensing convergence map contains 301 peaks with signal-to-noise ratio ν > 3.5, consistent with predictions of a ΛCDM model. Of these peaks, 126 lie within 3 0 of a brightest central galaxy identified from multicolor optical imaging in an independent, red sequence survey. We also identify seven counterparts for massive clusters previously seen in X-ray emission within 6 deg 2 XMM-LSS survey. With photometric redshift estimates for the source galaxies, we use a tomographic lensing method to fit the redshift and mass of each convergence peak. Matching these to the optical observations, we confirm 85 groups/clusters with χ 2 reduced < 3.0, at a mean redshift 〈 z c 〉 = 0.36 and velocity dispersion 〈σ c 〉 = 658.8 km s −1 . Future surveys, such as DES, LSST, KDUST, and EUCLID, will be able to apply these techniques to map clusters in much larger volumes and thus tightly constrain cosmological models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".