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.
@article{arxiv.2511.14667,
title = {High-resolution weak lensing mass mapping from DES-Y3 data using diffusion-based prior},
author = {Supranta S. Boruah and Michael Jacob and Bhuvnesh Jain and Riya Maiya and Raghav Venkataramanan},
journal= {arXiv preprint arXiv:2511.14667},
year = {2025}
}
Comments
6 pages, 3 figures, Accepted at Neurips Machine Learning and the Physical Sciences workshop