Cosmic variance of weak lensing surveys in the non-Gaussian regime
Bibliographic record
Abstract
The results from weak gravitational lensing analyses are subject to a cosmic variance error term that has previously been estimated assuming Gaussian statistics. In this letter we address the issue of estimating cosmic variance errors for weak lensing surveys in the non-Gaussian regime. Using standard cold dark matter model ray-tracing simulations characterized by Ωm = 0.3, ΩΛ = 0.7, h = 0.7, σ8 = 1.0 for different survey redshifts zs, we determine the variance of the two-point shear correlation function measured across 64 independent lines of sight. We compare the measured variance to the variance expected from a random Gaussian field and derive a redshift-dependent non-Gaussian calibration relation. We find that the ratio between the non-Gaussian and Gaussian variance at 1 arcminute can be as high as ∼ 30 for a survey with source redshift zs ∼ 0.5 and ∼ 10 for zs ∼ 1. The transition scale ϑc above which the ratio is consistent with unity, is found to be ϑc ∼ 20 arcmin for zs ∼ 0.5 and ϑc ∼ 10 arcmin for zs ∼ 1. We provide fitting formula to our results permitting the estimation of non-Gaussian cosmic variance errors and discuss the impact on current and future surveys. A more extensive set of simulations will however be required to investigate the dependence of our results on cosmology, specifically on the amplitude of clustering. Key words: cosmology: theory- gravitational lenses- large-scale structure
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".