用物理引导的扩散模型，快速精准重建星系团质量分布。
Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models
- 基于1.5万组模拟数据训练扩散先验，学习光与暗物质关联。
- 分钟级完成重建，精度超越人工调参方法，匹配知名星系团结果。
- 适合大规模巡天数据处理，支持未来宇宙学研究需求。
星系团是通过引力透镜探测天体物理与宇宙学的重要工具：其质量中85%由暗物质主导，会扭曲背景光线。然而，当前的质量重建方法缺乏可扩展性与大规模基准，难以处理未来宽视场巡天预计的数十万星系团。本文提出一种全自动方法，从测光与引力透镜观测中重建星系团表面质量密度。核心是构建了包含15,000个模拟星系团的DarkClusters-15k数据集，为迄今最大基准，覆盖多红移与多种模拟框架。我们在该数据集上训练了一个即插即用的扩散先验，学习质量与光之间的统计关系，并在弱/强引力透镜观测约束下生成后验样本，实现基于显式物理的合理重建与校准的不确定性估计。本方法无需专家调参，运行仅需数分钟，准确率更高，且达到对MACS 1206星系团的人工精细调参水平。我们公开发布方法与DarkClusters-15k数据集，以支持未来宽视场宇宙学巡天的研究与发展。
原文摘要 · Abstract (English)
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.
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