A general non-perturbative field-level posterior is constructed and expanded around its Gaussian limit to express Fisher information in terms of connected correlators, recovering standard results for power spectrum and bispectrum while quantifying compression losses.
arXiv: 2303.13056 , eprint =
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Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
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On the Relation Between Field-Level Posteriors, Correlators, and their Likelihoods
A general non-perturbative field-level posterior is constructed and expanded around its Gaussian limit to express Fisher information in terms of connected correlators, recovering standard results for power spectrum and bispectrum while quantifying compression losses.
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Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.