# Bayesian weak lensing tomography: Reconstructing the 3D large-scale distribution of matter with a lognormal prior

## TL;DR

This paper introduces a Bayesian method for 3D matter distribution reconstruction from weak lensing data, comparing Gaussian and lognormal priors, and demonstrating improved accuracy with the lognormal model in low-noise, small-scale scenarios.

## Contribution

The work develops a Bayesian reconstruction algorithm using a lognormal prior for non-Gaussian density fields, outperforming Gaussian priors in certain realistic weak lensing scenarios.

## Key findings

- Lognormal prior enforces non-negative densities.
- Lognormal model better captures skewness and extremal values.
- Higher pixel-wise correlation with true density in low-noise, small-scale cases.

## Abstract

We present a Bayesian reconstruction algorithm that infers the three-dimensional large-scale matter distribution from the weak gravitational lensing effects measured in the image shapes of galaxies. The algorithm is designed to also work with non-Gaussian posterior distributions which arise, for example, from a non-Gaussian prior distribution. In this work, we use a lognormal prior and compare the reconstruction results to a Gaussian prior in a suite of increasingly realistic tests on mock data. We find that in cases of high noise levels (i.e. for low source galaxy densities and/or high shape measurement uncertainties), both normal and lognormal priors lead to reconstructions of comparable quality, but with the lognormal reconstruction being prone to mass-sheet degeneracy. In the low-noise regime and on small scales, the lognormal model produces better reconstructions than the normal model: The lognormal model 1) enforces non-negative densities, while negative densities are present when a normal prior is employed, 2) better traces the extremal values and the skewness of the true underlying distribution, and 3) yields a higher pixel-wise correlation between the reconstruction and the true density.