DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
Title resolution pending
5 Pith papers cite this work, alongside 34 external citations. Polarity classification is still indexing.
5
Pith papers citing it
34
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
years
2026 5roles
background 1polarities
background 1representative citing papers
An accurate multivariate Student-t approximation matches covariance and kurtosis of the convolution of Sellentin–Heavens/Percival likelihoods with extra Gaussian noise, preserving heavy tails for cosmological inference.
A statistical reanalysis of 143 Y_p determinations from 1960s-2022 reveals long-term convergence with change points in the mid-2000s and early 2010s, plus significant non-independence among many extragalactic H II region measurements.
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
citing papers explorer
-
Dark Energy Survey Year 3 results: optimized $w$CDM simulation-based inference with weak lensing map-level hybrid statistics
DES Y3 weak lensing analysis with hybrid map-level statistics and simulation-based inference yields S8 = 0.808 ± 0.017, Ωm = 0.325 ± 0.024, and w < -0.766, improving the figure of merit by 60% over prior state-of-the-art.
-
On combining estimated and analytic covariance matrices
An accurate multivariate Student-t approximation matches covariance and kurtosis of the convolution of Sellentin–Heavens/Percival likelihoods with extra Gaussian noise, preserving heavy tails for cosmological inference.
-
Fifty years of primordial helium abundances: A statistical reanalysis
A statistical reanalysis of 143 Y_p determinations from 1960s-2022 reveals long-term convergence with change points in the mid-2000s and early 2010s, plus significant non-independence among many extragalactic H II region measurements.
-
Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
- Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics