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.
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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.
An approximate multivariate Student-t likelihood is derived for the convolution of an inverse-Wishart-based Student-t with Gaussian errors by matching covariance and multivariate kurtosis.
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
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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.