CARS: The CFHTLS-Archive-Research Survey
Bibliographic record
Abstract
Context. Weak gravitational lensing (WL) has been established as one of the most promising probes of cosmology. So far, most studies have exploited the shear effect of WL leading to coherent distortions of galaxy shapes. WL also introduces coherent magnifications.Aims. We want to detect this cosmic magnification effect (coherent magnification by the large-scale structure of the Universe) in large samples of high-redshift galaxies selected from the Deep part of the Canada-France-Hawaii-Telescope Legacy Survey (CFHTLS).Methods. Lyman-break galaxies (LBGs) selected by their colours to be at –5, are used as a background sample and are cross-correlated to foreground lens galaxies, which are selected by accurate photometric redshifts (photo-z's). The signals of LBGs in different magnitude bins are compared to predictions from WL theory. An optimally weighted correlation function is estimated by taking into account the slope of external LBG luminosity functions.Results. For the first time, we detect cosmic magnification in a sample of normal galaxies. These background sources are also the ones with the highest redshifts so far used for WL measurements. The amplitude and angular dependence of the cross-correlation functions agree well with theoretical expectations and the lensing signal is detected with high significance. Avoiding low-redshift ranges in the foreground samples which might contaminate the LBG samples we can make a measurement that is virtually free of systematics. In particular, we detect an anti-correlation between faint LBGs and foreground galaxies which cannot be caused by redshift overlap.Conclusions. Cross-correlating LBGs (and in future also photo-z selected galaxies) as background sources to well understood foreground samples based on accurate photo-z's will become a powerful cosmological probe in future large imaging surveys.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.129 | 0.102 |
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".