Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.
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Manticore-Deep uses tiled Bayesian field-level inference on SDSS and BOSS data to produce posterior ensembles of 3D cosmic fields that are consistent with LCDM and validated by 7.4σ CMB lensing and 3.5σ kSZ detections.
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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Towards Practical Field-Level Inference for Weak Lensing
Field-level inference from weak lensing maps yields significantly tighter cosmological constraints than power-spectrum analysis when using the same forward-modeling pipeline, especially on small scales.
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The Manticore Project II: Bayesian digital twins of cosmic structure across the SDSS and BOSS volumes
Manticore-Deep uses tiled Bayesian field-level inference on SDSS and BOSS data to produce posterior ensembles of 3D cosmic fields that are consistent with LCDM and validated by 7.4σ CMB lensing and 3.5σ kSZ detections.
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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.