DICES combines binary-space-partition equal-area jackknives, correlation-matrix shrinkage, and delete-2 diagonal correction to yield non-singular, debiased covariances for Euclid clustering and weak-lensing spectra, cutting relative error 33% (covariance) and 48% (correlation) versus plain jackknife
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A CNN for cosmological parameter estimation from large-scale structure relies on both Gaussian and non-Gaussian information, emphasizing scales at the linear-to-nonlinear transition.
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Euclid preparation. LXXXIX. Accurate and precise data-driven angular power spectrum covariances
DICES combines binary-space-partition equal-area jackknives, correlation-matrix shrinkage, and delete-2 diagonal correction to yield non-singular, debiased covariances for Euclid clustering and weak-lensing spectra, cutting relative error 33% (covariance) and 48% (correlation) versus plain jackknife
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Interpretability of deep-learning methods applied to large-scale structure surveys
A CNN for cosmological parameter estimation from large-scale structure relies on both Gaussian and non-Gaussian information, emphasizing scales at the linear-to-nonlinear transition.