Stage IV baryonic feedback correction for non-Gaussianity inference

TL;DR

This paper introduces a Bayesian model averaging approach to mitigate baryonic feedback uncertainties in non-Gaussian weak lensing analyses, enhancing the robustness of cosmological parameter inference for Stage IV surveys.

Contribution

It develops a Bayesian framework that combines multiple baryonic feedback models to improve the reliability of non-Gaussian weak lensing measurements.

Findings

BMA effectively reduces biases from baryonic physics in peak count analyses.

The approach improves the robustness of cosmological constraints under model uncertainty.

Demonstrated success in a Stage IV convergence peak counts context.

Abstract

Non-Gaussian statistics of the projected weak lensing field are powerful estimators that can outperform the constraining power of the two-point functions in inferring cosmological parameters. This is because these estimators extract the non-Gaussian information contained in the small scales. However, fully leveraging the statistical precision of such estimators is hampered by theoretical uncertainties, such as those arising from baryonic physics. Moreover, as non-Gaussian estimators mix different scales, there exists no natural cut-off scale below which baryonic feedback can be completely removed. We therefore present a Bayesian solution for accounting for baryonic feedback uncertainty in weak lensing non-Gaussianity inference. Our solution implements Bayesian model averaging (BMA), a statistical framework that accounts for model uncertainty and combines the strengths of different models to produce more robust and reliable parameter inferences. We demonstrate the effectiveness of this approach in a Stage IV convergence peak counts analysis, including three baryonic feedback models. We find that the resulting BMA posterior distribution safeguards parameter inference against biases due to baryonic feedback, and therefore provides a robust framework for obtaining accurate cosmological constraints at Stage IV precision under model uncertainty scenarios.