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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Calibrated NQE enables unbiased field-level cosmological inference from 2D density maps by training mostly on approximate PM simulations and calibrating with ~100 PP simulations.
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.
citing papers explorer
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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.
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Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulators
Calibrated NQE enables unbiased field-level cosmological inference from 2D density maps by training mostly on approximate PM simulations and calibrating with ~100 PP simulations.
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On combining estimated and analytic covariance matrices
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.