Bayesian mass mapping with weak lensing data using KARMMA -- validation with simulations and application to Dark Energy Survey Year 3 data

TL;DR

This paper presents an updated Bayesian mass-mapping method called KARMMA, validated with simulations and applied to DES Y3 data, providing more accurate convergence maps that preserve statistical properties and outperform traditional algorithms.

Contribution

The paper introduces an improved Bayesian mass-mapping algorithm that accounts for tomographic cross-covariance and lognormal priors, validated with simulations and applied to real survey data.

Findings

KARMMA produces more accurate convergence maps than traditional methods.

The posterior maps accurately reproduce statistical properties of the input density field.

Application to DES Y3 data shows the reconstructed shear correlation matches observed data.

Abstract

We update the field-level inference code KARMMA to enable tomographic forward-modelling of shear maps. Our code assumes a lognormal prior on the convergence field, and properly accounts for the cross-covariance in the lensing signal across tomographic source bins. We use mock weak lensing data from N-body simulations to validate our mass-mapping forward model by comparing our posterior maps to the input convergence fields. We find that KARMMA produces more accurate reconstructions than traditional mass-mapping algorithms. More-over, the KARMMA posteriors reproduce all statistical properties of the input density field we tested -- one- and two-point functions, and the peak and void number counts -- with $\lesssim~10\%$ accuracy. Our posteriors exhibit a small bias that increases with decreasing source redshift, but these biases are small compared to the statistical uncertainties of current (DES) cosmic shear surveys. Finally, we apply KARMMA to Dark Energy Survey Year 3 (DES-Y3) weak lensing data, and verify that the two point shear correlation function $\xi_+$ is well fit by the correlation function of the reconstructed convergence field. This is a non-trivial test that traditional mass mapping algorithms fail. The code is publicly available at https://github.com/Supranta/KaRMMa.git. KARMMA DES-Y3 mass maps are publicly available at https://zenodo.org/records/10672062.