用流匹配模型加速引力透镜模拟，200倍提速且保持高精度。
FlowLensing: Simulating Gravitational Lensing with Flow Matching
- 基于扩散变换器的流匹配模型，支持离散与连续参数。
- 相比传统模拟器提速超200倍，图像保真度高、推理延迟低。
- 适合暗物质结构探测和宇宙学大尺度巡天研究。
引力透镜是探测暗物质最有力的手段之一，但大规模生成高保真透镜图像仍存在瓶颈。现有工具依赖光线追踪或前向建模流程，虽精确但速度极慢。我们提出FlowLensing，一种基于扩散变换器的紧凑高效流匹配模型，用于强引力透镜模拟。该模型在离散与连续两种范式下运行，可处理不同暗物质模型及连续参数，确保物理一致性。通过实现可扩展模拟，模型显著推动了暗物质研究，尤其适用于宇宙学巡天中对暗物质子结构的探测。实验表明，相较于经典模拟器，本模型在复杂暗物质模型下实现超过200倍的加速，同时保持高保真度与低推理延迟。FlowLensing实现了快速、可扩展且物理一致的图像生成，为传统前向建模流程提供了实用替代方案。
原文摘要 · Abstract (English)
Gravitational lensing is one of the most powerful probes of dark matter, yet creating high-fidelity lensed images at scale remains a bottleneck. Existing tools rely on ray-tracing or forward-modeling pipelines that, while precise, are prohibitively slow. We introduce FlowLensing, a Diffusion Transformer-based compact and efficient flow-matching model for strong gravitational lensing simulation. FlowLensing operates in both discrete and continuous regimes, handling classes such as different dark matter models as well as continuous model parameters ensuring physical consistency. By enabling scalable simulations, our model can advance dark matter studies, specifically for probing dark matter substructure in cosmological surveys. We find that our model achieves a speedup of over 200$\times$ compared to classical simulators for intensive dark matter models, with high fidelity and low inference latency. FlowLensing enables rapid, scalable, and physically consistent image synthesis, offering a practical alternative to traditional forward-modeling pipelines.
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