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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Cross-covariances and cross-correlations between Euclid spectroscopic and photometric probes have negligible impact on cosmological parameter constraints.
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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.
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Euclid preparation: 6x2 pt analysis of Euclid's spectroscopic and photometric data sets
Cross-covariances and cross-correlations between Euclid spectroscopic and photometric probes have negligible impact on cosmological parameter constraints.