High-resolution weak lensing mass mapping from DES-Y3 data using diffusion-based prior

TL;DR

This paper introduces a diffusion-based prior method for high-resolution mass mapping from DES-Y3 weak lensing data, enabling detailed cosmic structure reconstruction with unbiased results and uncertainty quantification.

Contribution

It develops a novel diffusion model framework for mass map reconstruction from weak lensing data, improving resolution and bias correction over previous methods.

Findings

Mass maps show enhanced cosmic structure detail.

Bias in standard DPS results can be corrected by score scaling.

Uncertainty quantification is effectively integrated.

Abstract

High-resolution mapping of cosmic mass distribution is essential for a variety of astrophysical applications including understanding cosmic structure formation, and galaxy formation and evolution. However dark matter is not directly observed and therefore we need advanced methods for solving inverse problems to reconstruct the underlying cosmic matter distribution. Here, we train a generative diffusion model and use it in the Diffusion Posterior Sampling (DPS) framework to reconstruct mass maps from Dark Energy Survey-Year 3 (DES-Y3) weak gravitational lensing data at high (1 arcminute) resolution. We show that the standard DPS results are biased, but they can be easily corrected by scaling the log-likelihood score during the diffusion process, yielding unbiased results with proper uncertainty quantification. The resulting mass maps reveal cosmic structures with enhanced detail, opening the door for improved astrophysical studies using the obtained mass maps.