Cosmic shear analysis in 50 uncorrelated VLT fields. Implications for $\Omega_0$, $\sigma_8$
Bibliographic record
Abstract
We observed with the camera FORS1 on the VLT (UT1, ANTU) 50 randomly selected fields and analyzed the cosmic shear inside circular apertures with diameter ranging from 0.5 to 5.0 arcmin. The images were obtained in optimal conditions using the Service Observing proposed by ESO on the VLT, which enabled us to a well-defined and homogeneous set of data. The 50 fields cover a 0.64 square-degrees area spread over more than 1000 square-degrees obtain which provides a sample ideal for minimizing the cosmic variance. Using the same techniques as in Van Waerbeke et al. ([CITE]), we measured the cosmic shear signal and investigated the systematics of the VLT sample. We find a significant excess of correlations between galaxy ellipticities on those angular scales. The amplitude and the shape of the correlation as function of angular scale are remarkably similar to those reported so far. Using our combined VLT and CFHT data and adding the results published by other teams we put the first joint constraints on and using cosmic shear surveys. From a deduced average of the redshift of the sources the combined data are consistent with (for a CDM power spectrum and ), in excellent agreement with the cosmological constraints obtained from the local cluster abundance. This is consistent with theoretical expectations if the density field grew from initial Gaussian conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".