Accurate field-level weak lensing inference for precision cosmology

TL;DR

This paper introduces Miko, a pipeline for field-level weak lensing analysis that addresses systematics, model biases, and uncertainty calibration to enhance cosmological inference accuracy for future surveys.

Contribution

The paper develops a comprehensive pipeline for field-level weak lensing inference, identifying key systematics, and comparing Gaussian and log-normal priors for unbiased and well-calibrated cosmological parameters.

Findings

Systematics like aliasing and shape noise can be corrected to 1% accuracy.

Gaussian prior yields unbiased parameters but overconfident uncertainties.

Log-normal prior provides accurate uncertainties with proper calibration.

Abstract

We present $\texttt{Miko}$, a catalog-to-cosmology pipeline for general flat-sky field-level inference, which provides access to cosmological information beyond the two-point statistics. In the context of weak lensing, we identify several new field-level analysis systematics (such as aliasing, Fourier mode-coupling, and density-induced shape noise), quantify their impact on cosmological constraints, and correct the biases to a percent level. Next, we find that model misspecification can lead to both absolute bias and incorrect uncertainty quantification for the inferred cosmological parameters in realistic simulations. The Gaussian map prior infers unbiased cosmological parameters, regardless of the true data distribution, but it yields overconfident uncertainties. The log-normal map prior quantifies the uncertainties accurately, although it requires careful calibration of the shift parameters for unbiased cosmological parameters. We demonstrate systematics control down to the $2\%$ level for both models, making them suitable for ongoing weak lensing surveys.