@InProceedings{Royo2026mapping,
author="Royo, Diego
and Zhao, Brandon
and Mu{\~{n}}oz, Adolfo
and Gutierrez, Diego
and Bouman, Katherine L.",
editor="Favaro, Paolo
and Kukelova, Zuzana
and Maki, Atsuto
and Rohrbach, Anna
and Schindler, Konrad
and Tombari, Federico",
title="Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models",
booktitle="Computer Vision -- ECCV 2026",
year="2026",
publisher="Springer Nature Switzerland",
address="Cham",
pages="394--413",
abstract="Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: their mass, dominated by 85{\%} dark matter, distorts background light. Yet, mass reconstruction lacks the scalability and large-scale benchmarks to process the hundreds of thousands of clusters expected from forthcoming wide-field surveys. We introduce a fully automated method to reconstruct cluster surface mass density from photometry and gravitational lensing observables. Central to our approach is {\$}{\$}{\backslash}textsc {\{}DarkClusters-15k{\}}{\$}{\$}DARKCLUSTERS-15K, our new dataset of 15,000 mock cluster observations with paired mass and photometry maps, the largest to date, spanning multiple redshifts and simulation frameworks. We train a plug-and-play diffusion prior on DarkClusters-15k that learns the statistical relationship between mass and light, and draw approximate posterior samples constrained by weak- and strong-lensing observables, yielding principled reconstructions with well-calibrated empirical uncertainties. Our approach requires no expert tuning, runs in minutes rather than hours, achieves higher accuracy, and matches expert-tuned reconstructions of the MACS 1206 cluster. We release our method and DarkClusters-15k to support upcoming wide-field cosmological surveys.",
isbn="978-3-032-37044-0"
}