Cosmic variance of weak lensing surveys in the non-Gaussian regime
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 and σ8 = 1 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 arcmin 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 formulae 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it