Cosmological constraints from the 100-deg2 weak-lensing survey
Bibliographic record
Abstract
Abstract We present a cosmic shear analysis of the 100-deg2 weak-lensing survey, combining data from the CFHTLS-Wide, RCS, VIRMOS-DESCART and GaBoDS surveys. Spanning ∼100 deg2, with a median source redshift z∼ 0.78, this combined survey allows us to place tight joint constraints on the matter density parameter Ωm, and the amplitude of the matter power spectrum σ8, finding σ8(Ωm/0.24)0.59= 0.84 ± 0.05. Tables of the measured shear correlation function and the calculated covariance matrix for each survey are included as supplementary material to the online version of this article. The accuracy of our results is a marked improvement on previous work owing to three important differences in our analysis; we correctly account for sample variance errors by including a non-Gaussian contribution estimated from numerical simulations; we correct the measured shear for a calibration bias as estimated from simulated data; we model the redshift distribution, n(z), of each survey from the largest deep photometric redshift catalogue currently available from the CFHTLS-Deep. This catalogue is randomly sampled to reproduce the magnitude distribution of each survey with the resulting survey-dependent n(z) parametrized using two different models. While our results are consistent for the n(z) models tested, we find that our cosmological parameter constraints depend weakly (at the 5 per cent level) on the inclusion or exclusion of galaxies with low-confidence photometric redshift estimates (z > 1.5). These high-redshift galaxies are relatively few in number but contribute a significant weak-lensing signal. It will therefore be important for future weak-lensing surveys to obtain near-infrared data to reliably determine the number of high-redshift galaxies in cosmic shear analyses.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".