Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models

TL;DR

This paper presents a physics-guided diffusion model for automated galaxy cluster mass reconstruction, leveraging a large simulated dataset to improve accuracy and scalability for upcoming cosmological surveys.

Contribution

It introduces DarkClusters-15k, the largest simulated cluster dataset, and a diffusion-based method that learns mass-light relationships for efficient, physics-informed mass mapping.

Findings

Achieves higher accuracy than existing methods.

Runs in minutes instead of hours.

Provides well-calibrated uncertainties.

Abstract

Galaxy clusters are powerful probes of astrophysics and cosmology through gravitational lensing: the clusters' 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 DarkClusters-15k, our new dataset of 15,000 simulated clusters with paired mass and photometry maps, the largest benchmark 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 posterior samples constrained by weak- and strong-lensing observables; this yields principled reconstructions driven by explicit physics, alongside well-calibrated uncertainties. Our approach requires no expert tuning, runs in minutes rather than hours, achieves higher accuracy, and matches expertly-tuned reconstructions of the MACS 1206 cluster. We release our method and DarkClusters-15k to support development and benchmarking for upcoming wide-field cosmological surveys.